EntityPatternProvider
instance construct the regexesDataSource
implementations.Inpainter
implementations that consume a mask image
(rather than connected components or pixel sets).MaskedObject
.null
mask.DataSource
backed by multiple lists of data.MultiScaleObjectDetector
.MultivariateGaussian
implementationsOctave
s using the approach in
Section 4 of Lowe's IJCV paper (minus the bit on using Brown's interpolation
approach to improve localisation).LocalFeature
s as they
are extracted from Octave
s.Point2d
implementations that retains the underlying
precision.SearchResource.search(Query)
function periodically and offers all
discovered Status
instances with the underlying
BlockingDroppingBufferedStream
.AbstractValueAnimator.nextValue()
is
called, subject to some constraints.Location
should be accepted
or rejected.AccumulatingImageCombiner
.MapBackedDataset
returned by MapBackedDataset.IdentifiableBuilder.build()
where the key
is the identifier returned by Identifiable.getID()
.FImage
to the pixels of this
image.FImage
that contains the pixels of this image
increased by the given value.TokenAnnotation
s matched by this
NamedEntityAnnotation
PointList
.PointList
.Reference
Reference
or References
from the given
class.Reference
or References
from the given
method.Reference
sSerialDataListener
to the listener list.AudioStream
to this mixer.AdjustedRandomIndexAnalysis
instanceAestheticode
HarrisIPD
detector with scales 2.0f and 2.0f *
1.4f.AffineAligner
attempts to find an affine transform that will warp
the face to the canonical frame by aligning facial keypoints.Keypoint
that holds the AffineParams
and
simulation index of the affine simulation from which it was detected.KeypointLocation
extended to hold a rotation, tilt and index
corresponding to an affine simulation.AffineTransformModel
AffineTransformModel
.AffineTransformModel3d
TextPipeAnnotation
has been added to all members in a list of TextPipeAnnotation
.NullOrientationExtractor
) and uses the
IrregularBinningSIFTFeatureProvider
.Evaluator.evaluate()
and return the analysed data.RetrievalEngine
against a
ground-truth set of relevant results and produce an
AnalysisResult
which can be read by a human.PixelAnalyser
.ImageAnalyser
.PixelAnalyser
.PixelAnalyser
, only analysing
those pixels where the mask is non-zero.DecisionAnalysis
, construct an analysisAnimatedVisualisationProvider
.TextPipeAnnotation
.AnnotatedListHelper
with the given list.Annotated
.Annotator
using
standardised classification and/or retrieval evaluation methodologies.AnnotationEvaluator
with the given annotator and
test data (with ground-truth annotations).FaceRecogniser
built on top of an IncrementalAnnotator
.Point2ds
are collinear.DataSource
backed by an array.FeatureVector
that are backed by
a native array.Comparator
for sorting edges in ascending orderDoGSIFTEngineOptions
are defaultEngine
for ASIFT.DoGSIFTEngine
.DoGSIFTEngine
.List
view of the given dataset.MLArray
to a Matrix
IntRandomForest.assignWord(int[])
function to construct the word
representing this data point.BufferedImage
to an Image
.Matrix
to a SparseRowMatrix
AudioGrabber
.AudioStream
s and mixes
then with some gain compensation into a single audio stream.VideoDisplay.EndAction
HardAssigner
with an associated codebook.SparseFloatFV
instances for a list of words.BarVisualisation
can be used to draw to an image a bar graph of
any data set to an RGBA MBFImage.HaarFeatureType.X2Y2
)FImage
s.DoGSIFTEngine
, with the exception of the option to double the
size of the initial image which can be overridden.DoGSIFTEngine
.ClassificationResult
that internally
maintains a map of classes to confidences.MeasuresBeatsTicksTimecode
s that allow
the position within a music score to be tracked.AbstractOctaveExtremaFinder
that searches for local extrema in scale space.BasicOctaveExtremaFinder.DEFAULT_MAGNITUDE_THRESHOLD
for the
magnitude threshold and AbstractOctaveExtremaFinder.DEFAULT_EIGENVALUE_RATIO
for the
Eigenvalue ratio threshold.AbstractOctaveExtremaFinder.DEFAULT_EIGENVALUE_RATIO
for the Eigenvalue ratio threshold.AbstractOctaveInterestPointFinder
that detects points on a regular
grid.Annotator
that is trained in "batch" mode; all training examples
are presented at once.BatchExtractor
tool.AbstractOctaveExtremaFinder#process(OCTAVE)
Reference
annotations.BilinearSparseOnlineLearner
WindowedHistogramExtractor
with the primary
purpose of of producing efficient access to histograms of arbitrary windows
of the image.BipolarSentiment.State.NEUTRAL
BipolarSentiment
which is either positive or negativeStream
with an internal buffer based on a
BlockingDroppingQueue
.Queue
that additionally supports operations that wait for the queue
to become non-empty when retrieving an element, and drop the oldest element
from the queue when storing a new element if space is not available for the
new element.BlockSpatialAggregator
performs spatial pooling of local features
by grouping the local features into non-overlapping, fixed-size spatial
blocks, and applying a VectorAggregator
(i.e.InvertedPriorityQueue
.BoundedPriorityQueue
with the specified initial
capacity that orders its elements according to the inverse of the
specified comparator.BoundedPriorityQueue
with the specified initial
capacity that orders its elements according to their inverse
natural ordering.TreeSet
that has a bounded upper size.ProgrammaticBrowser
to hand javascript
messages to.MapBackedDataset
.VLADIndexerData
using the information provided at
construction time.RemapProcessor
capable of correcting the radial and
tangential distortion of this camera.RemapProcessor
capable of correcting the radial and
tangential distortion of this camera.RemapProcessor
capable of correcting the radial and
tangential distortion of this camera.RemapProcessor
capable of correcting the radial and
tangential distortion of this camera.VLAD
using the information provided at construction time.WorldPlace
given a country name.WorldPlace
given a country code.DataSource
backed by a 2D array of bytes.ValueAnimator
capable of producing byte arrays from
a set of potentially independent underlying ValueAnimator
s.ValueAnimator
sSpatialClusterer
that just produces a flat set of centroids.LocalFeature
s typed on ByteFV
by rejecting those that
have a low feature entropy.RandomisedHashFunction
for hashing byte arrays.RandomisedHashFunction
for producing hash functions
seeded by random numbers.ByteKDTree.BBFMedianSplit
)ByteKMeans
used primarily as a convenience function for reading.SoftAssigner
that picks a fixed number of nearest neighbours.Sketcher
that produces bit-string sketches encoded as byte arrays.NearestNeighboursFactory
for producing
ByteNearestNeighboursExact
s.NearestNeighboursFactory
for producing
ByteNearestNeighboursKDTree
s.ByteProductQuantiser
with the given
nearest-neighbour assigners.ByteProductQuantiser
using
(Exact) K-Means.Cachable
that is capable of being read/written as ASCII.Cachable
that is capable of being read/written as Binary.DoubleSpectralClustering
extention which knows how to write and read its eigenvectors to disk
and therefore not regenerate them when calling the underlying PreparedSpectralClustering
HashMap
.HashMap
.HashMap
The given extractor will be used to
generate the features.Map
for each rater where the keys represent
all the subjects that were rated by the raters and the values represent
the annotations given by the raters.Map
for each rater where the keys represent
all the subjects that were rated by the raters and the values represent
the annotations given by the raters.PointList
PointList
.InputStream
can be read by the
given InputStreamObjectReader
.ObjectReader
.MBFImage
PImage
PImage
DetectedFace
that is represented/detected by a
ConnectedComponent
.ConnectedComponent
describing the shape of the detected face.Matrix
.InterestPointData
away if two instances are similar.FastChessboardDetector.analyseImage(FImage)
was likely to contain a suitable chessboard pattern.ImageCombiner
to calculate a colour disparity map between
two images.ClassificationAnalyser
is used to analyse the raw
results from a Classifier
in the context of
a ClassificationEvaluator
and to produce an AnalysisResult
describing the performance of the ClassificationEvaluator
.Evaluator
for the evaluation of classification
experiments.ClassificationEvaluator
with the given
classifier, set of objects to classify, ground truth ("actual") data and
an ClassificationAnalyser
.ClassificationEvaluator
with the given
classifier, ground truth ("actual") data and an
ClassificationAnalyser
.ClassificationEvaluator
with the given
classifier, ground truth ("actual") data and an
ClassificationAnalyser
.ClassificationEvaluator
with the given
pre-classified results, the ground truth ("actual") data and an
ClassificationAnalyser
.Classifier
.ClassificationResult
sClassLoaderTransform
provides an alternative to using a java agent to
perform byte-code manipulation by providing a classloader that will
automatically transform classes as they are loaded.ImageAnalyser
for GPGPU accelerated analysis.String
s.String
s.Image
types
and CLImage2D
s for GPGPU acceleration.ImageProcessor
for GPGPU accelerated processing.String
s.String
s.min
to zero or above
max
to the highest normal value that the image allows
(usually 1 for floating-point images).min
to zero or above
max
to the highest normal value that the image allows
(usually 1 for floating-point images).min
to zero or above
max
to the highest normal value that the image allows
(usually 1 for floating-point images).CLMDetectedFace
to the neutral pose
(reference shape) of the CLMFaceDetector.Configuration
.CLMAligner
using the default
CLMFaceDetector.Configuration
and default size of 100 pixels.CLMAligner
using the default
CLMFaceDetector.Configuration
and given size for the aligned output image.CLMAligner
using the provided
CLMFaceDetector.Configuration
and default size of 100 pixels.CLMDetectedFace
by copying the state from a
MultiTracker.TrackedFace
CLMDetectedFace
sCLMPoseFeature
with the given feature vector.FacialFeatureExtractor
for providing CLMPoseFeature
s.CLMPoseShapeFeature
with the given feature vector.FacialFeatureExtractor
for providing
CLMPoseShapeFeature
s.CLMShapeFeature
with the given feature vector.FacialFeatureExtractor
for providing CLMShapeFeature
s.DataSource
as the centroids of the clusters.DataSource
as the centroids of the clusters.DataSource
as the centroids of the clusters.DataSource
as the centroids of the clusters.DataSource
as the centroids of the clusters.DataSource
as the centroids of the clusters.DataSource
as the centroids of the clusters.DataSource
as the centroids of the clusters.DataSource
as the centroids of the clusters.DataSource
as the centroids of the clusters.DataSource
as the centroids of the clusters.DataSource
as the centroids of the clusters.ClusterAnalyser
instancesIntRAC
but explicitly specify the limit the number of
clusters.ClusterQuantiser
tool.ClusterType
.ResultAggregator
for collecting multiple CMResult
s
and producing a single unified report.ClassificationAnalyser
that creates Confusion Matrices.CMAnalyser
.ConfusionMatrix
.Stream
based on any Collection
of items.DoGSIFTEngine
.DoGSIFTEngine
, with the exception of the option to double the
size of the initial image which can be overridden.Engine
for Colour ASIFT.DoGSIFTEngine
.DoGSIFTEngine
.ColourContrast
feature extractor using the default
settings for the FelzenszwalbHuttenlocherSegmenter
.ColourContrast
feature extractor with the given
parameters for the underlying FelzenszwalbHuttenlocherSegmenter
.DenseSIFT
extractor to apply to
each band of the image created by converting the input to
#analyseImage(MBFImage)
or
ColourDenseSIFT.analyseImage(MBFImage, Rectangle)
to the given
ColourSpace
.ColourMap
.Iterable
over the columnsTimeFrequencyHolder.TimeFrequency
instance, keep count of cumulative
frequency and set the periodFrequency to the one furthest along in
timeStreamCombiner
from the given streamsRegexUtil.regex_or_match(java.util.List)
IdentityReducer
class.ImageCombiner
.VideoShotDetector.setFPS(double)
before you process
any frames.Comparator
for Comparable
objects.ByteKMeans
instancea
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.null
).Histogram
object, which must have the same length as given
by WindowedHistogramExtractor.getNumBins()
.Histogram
object, which must have the same length as given
by WindowedHistogramExtractor.getNumBins()
.PointList
s.PointList
s.HardAssigner
.Iterable
s.Iterable
s.Iterator
s.List
view built on top of a list of underlying lists.AbstractOctaveLocalFeatureCollector
that collects LocalFeature
s in the form of LocalFeatureImpl
with the feature vector provided by the given feature extractor, and the
Location
provided by a ScaleSpaceLocation
with an x, y and
scale coordinates.ConfigurableRendererMono
and
ConfigurableRendererRGB
classes.ConnectedComponent
.Pixel
s.ConnectedComponent
s, this enum
determines and specifies how the boundary is calculated; either using a
4-connected rule, or an 8-connected rule.ConnectedComponent.ConnectMode
.ConnectedComponent.ConnectMode
.ConnectedComponent.ConnectMode
.ConnectedComponent
s.ConnectedComponent
.Segmenter
s by
applying the thresholding operation and then applying connected component
labeling.PircBot.onMessage(java.lang.String, java.lang.String, java.lang.String, java.lang.String, java.lang.String)
InterestPointFeatureCollector
ParameterizedSparqlString
Map
which can give typed elements and can fail
gracefully when elements don't exist.Context
.Context
.ContextAwareInitStrategy
is told the learner it is initialising against
and the current INDEPENDANT and DEPENDANT variables at init time.ContextFunctionAdaptor
that reads data from a given
key, applies a function and sets the result in a different key.ContextFunctionAdaptor
that reads data from a given
key, applies a function and sets the result with the same key.KeyContextInsertor
.Context
entry using a
predicateKeyContextExtractor
with the given key.TwitterSearchDataset
to support multiple queries.KeyContextExtractor
using the given key.null
type.Iterable
over all the contours belonging to this root
(including the root itself)MBFImageRenderer
for drawing Contour
s.MBFImage
to an RGBA CLImage2D
.FImage
to CLImage2D
.Image
to CLImage2D
.CLImage2D
to an MBFImage
.CLImage2D
to an FImage
.CLImage2D
to an Image
.Keypoint
s to FloatKeypoint
s.MultiTracker.TrackedFace
s to
CLMDetectedFace
s.MultiTracker.TrackedFace
s to
CLMDetectedFace
s.CLMDetectedFace
into
a MultiTracker.TrackedFace
.DenseMatrix
to a Matrix
.DenseMatrix
to a Matrix
.FeatureVector
to an array of Feature
s.TimeSeries
TimeSeries
FeatureVector
to an array of doubles using
FeatureVector.asDoubleVector()
.FloatBuffer
back into an
MBFImage
.FloatBuffer
back into an
FImage
.MBFImage
to a FloatBuffer
containing packed
RGBA pixels.FloatBuffer
containing packed
intensity pixels.BufferedImage
of any type, to BufferedImage
of
a specified type.CoordinateIndex
that performs
searching by brute-force comparison over the indexed coordinates.Coordinate
s that can
have points added to it and can be searched in a variety of ways.IBasicBolt
whose purpose is to increment a count on
the reciept of a tuple followed by an emit of the same tuple.IdentityReducer
but constructs a time index
found in CountTweetsInTimeperiod.TIMEINDEX_FILE
CountWordsAcrossTimeperiod.Map
and
CountWordsAcrossTimeperiod.Reduce
.CovarPrincipalComponentAnalysis
that
will extract all the eigenvectors.CovarPrincipalComponentAnalysis
that
will extract the n best eigenvectors.FVProviderExtractor
with the given extractor.AnnotatorFaceRecogniser
instances
from an annotator.EigenFaceRecogniser
with a
standard KNN classifier, incorporating a threshold on the maximum
distance (or minimum similarity) to allow a match.EigenFaceRecogniser
with a
standard KNN classifier.FaceRecognitionEngine
with the given face detector and
recogniser.FisherFaceRecogniser
with a
standard KNN classifier.FisherFaceRecogniser
with a
standard KNN classifier, incorporating a threshold on the maximum
distance (or minimum similarity) to allow a match.FaceSimilarityEngine
from the
specified detector, extractor and comparator.NearestNeighbours
object that works over the provided
data.AnnotatedObject
with the given object and its
annotations.AnnotatedObject
with the given object and its
annotation.KNNAnnotator
with the given extractor, comparator
and threshold.KNNAnnotator
with the given extractor and
comparator.KNNAnnotator
with the given extractor, comparator
and number of neighbours.KNNAnnotator
with the given extractor, comparator,
number of neighbours and threshold.NaiveBayesAnnotator
in the case
where the raw objects are themselves the feature and thus an
IdentityFeatureExtractor
can be used.ContextFunctionAdaptor
that reads data from a given
key, applies a function and sets the result in a different key.ContextOperationAdaptor
.HashFunction
.MetaPayload
created from the given payload and
metadataBufferedImage
.BufferedImage
.BufferedImage
.BufferedImage
.CrossValidationIterable
from the dataset.ByteKMeans
.ByteKMeans
.DoubleKMeans
.DoubleKMeans
.ByteKMeans
.ByteKMeans
.FloatKMeans
.FloatKMeans
.IntKMeans
.IntKMeans
.LongKMeans
.LongKMeans
.ShortKMeans
.ShortKMeans
.YagoEntityCandidateFinderFactory.YagoEntityCandidateFinder
given a path Yago Entity
Alias textfileSinglebandImageProcessor
that performs a Gaussian
blurring with a standard deviation given by sigma.IncrementalFloatADCNearestNeighbours
pre-prepared to
index dataByteKMeans
using an ensemble of KD-Trees to perform nearest-neighbour lookup.DoubleKMeans
using an ensemble of KD-Trees to perform nearest-neighbour lookup.FloatKMeans
using an ensemble of KD-Trees to perform nearest-neighbour lookup.IntKMeans
using an ensemble of KD-Trees to perform nearest-neighbour lookup.LongKMeans
using an ensemble of KD-Trees to perform nearest-neighbour lookup.ShortKMeans
using an ensemble of KD-Trees to perform nearest-neighbour lookup.LandmarkModel
.LandmarkModel
.ListDataset
of features from the given
ListDataset
of objects by extracting the features from the
objects with the given feature extractor.GroupedDataset
of keys to ListDataset
of
features from the given GroupedDataset
of keys to
ListDataset
s of objects by extracting the features from the
objects with the given feature extractor.AnnotatedObject
.AnnotatedObject
.WesternScaleNote
from
which other information can be garnered.WesternScaleNote
from
which other information can be garnered.WesternScaleNote
given a frequency.WesternScaleNote
from which other
information can be garnered.ImageRenderer
capable of drawing into this image.ImageRenderer
capable of drawing into this image.SampleBuffer
.SampleBuffer
.DataSource.getRandomRows(Object[])
and DataSource.getData(int, int, Object[])
methods.HomogeneousKernelMap.ExtractorWrapper
that applies the map to features
extracted by an internal extractor.RunnableExperiment
for performing cross-validation experiments on
face recognisers & classifiers.CrossValidationBenchmark
experiment with the given
dependent variables.BilinearSparseOnlineLearner
instances.BilinearSparseOnlineLearner
instances.AbstractTwitterPreprocessingToolOptions
from the
HadoopTwitterPreprocessingTool.ARGS_KEY
variable (once per in memory
mapper) and uses these to preprocess tweets.LoggerUtils.format(Logger, String, Level, Object...)
with level
Level.DEBUG
DecisionAnalysis
instanceDecisionAnalysis
instanceDecisionAnalysis
instanceTriangleFilter
(bilinear-interpolation filter) used
by instances of ResizeProcessor
, unless otherwise specified.TriangleFilter
(bilinear-interpolation filter) used
by instances of ResizeProcessor
, unless otherwise specified.BrowserDelegate
that does nothing other
than log any javascript calls, etc.TokenFactory
that loads the token
parameters from the default Java user preference store or interactively
queries the user for the required token parameters if the token has not been
used before.Client.delete(String)
on the underlying Client
instance.getInstance().deleteToken(tokenClass)
getInstance().deleteToken(tokenClass, name)
FImage
s.RunnableExperiment
instance.Comparator
for sorting edges in descending orderObjectDetector
.Detector
with the given parameters.Detector
with the given tree of stages and scale
factor.Detector
with the given tree of stages, and the
default parameters for step sizes and scale factor.MultivariateGaussian
with a diagonal covariance
matrix.StructuringElement.BOX
elementDiscreteCountBipolarSentiment
based on the (presumebly more complex) sentiment
heldDisplayUtilities.createNamedWindow(String)
DisplayUtilities.createNamedWindow(String)
DisplayUtilities.ImageComponent
that scales the displayed image.DistanceClusterer
clusters data that can be represented as a distance
matrix.ResidualCalculator
that uses a
DistanceComparator
to compute the error between the predicted and
observed data point.DistanceComparator
.DBSCAN
using a SparseMatrix
of distancesDmoz2CSV
.DoGSIFTEngine
extended to colour images (aka Colour-SIFT).DoGSIFTEngineOptions
.OctaveInterestPointFinder
that
can be applied to GaussianOctave
s.FacialFeature
that uses DoG-SIFT features to
describe a face.FacialFeatureExtractor
for producing DoGSIFTFeature
sFacialFeatureComparator
for comparing DoGSIFTFeature
s.DataSource
backed by a 2D array of doubles.KernelPerceptron
which works with
double arrays.ValueAnimator
capable of producing double arrays from
a set of potentially independent underlying ValueAnimator
s.ValueAnimator
sSpatialClusterer
that just produces a flat set of centroids.DBSCANClusters
which also holds the original dataSimilarityClusterer
around a datasetRandomisedHashFunction
for hashing double arrays.RandomisedHashFunction
for producing hash functions
seeded by random numbers.DoubleKDTree.BBFMedianSplit
)DoubleKDTreeClusterer.DoubleKDTreeClusterer()
with 0.01DoubleKMeans
used primarily as a convenience function for reading.SoftAssigner
that picks a fixed number of nearest neighbours.NearestNeighboursFactory
for producing
DoubleNearestNeighboursExact
s.NearestNeighboursFactory
for producing
DoubleNearestNeighboursKDTree
s.DoubleProductQuantiser
with the given
nearest-neighbour assigners.ByteProductQuantiser
using
(Exact) K-Means.DoubleTimeSeries
which may not be synchronised.DoubleTimeSeries
StatusConsumer
on Status
recievedStatusConsumer
instancesMapper
for downloading files.ChessboardCornerFinder.analyseImage(FImage)
on the given image.CLMDetectedFaceRenderer.drawDetectedFace(MBFImage,int, CLMDetectedFace)
but with the
insides of a MultiTracker.TrackedFace
.(x1,y1)
at an
angle of theta
with the given length, thickness and colour.(x1,y1)
at an
angle of theta
with the given length and colour.(x0,y0)
to the
coordinates specified by (x1,y1)
using the given color and
thickness.(x0,y0)
to
(x1,y1)
using the given colour.(x1,y1)
at an
angle of theta
with the given length, thickness and colour.(x0,y0)
to the
coordinates specified by (x1,y1)
using the given color and
thickness.(x1,y1)
at an
angle of theta
with the given length, thickness and colour.(x1,y1)
at an
angle of theta
with the given length and colour.(x0,y0)
to the
coordinates specified by (x1,y1)
using the given color and
thickness.(x0,y0)
to the
coordinates specified by (x1,y1)
using the given color and
thickness.(x0,y0)
to
(x1,y1)
using the given colour.(x0,y0)
to
(x1,y1)
using the given colour.(x0,y0)
to
(x1,y1)
using the given colour and thickness.Path2d
object(x1,y1)
at an
angle of theta
with the given length, thickness and colour.(x0,y0)
to the
coordinates specified by (x1,y1)
using the given color and
thickness.(x0,y0)
to the
coordinates specified by (x1,y1)
using the given color and
thickness.Image.drawLine(Path2d, int, Object)
with the given colour and thickness.ImageRenderer.drawLine(Path2d, int, Object)
with the given colour and thickness.Path2d
objectImage.drawLine(Path2d, int, Object)
with the given colour and thickness.ImageRenderer.drawLine(Path2d, int, Object)
with the given colour and thickness.Image.drawPoint(Point2d, Object, int)
with the given colour and size.ImageRenderer.drawPoint(Point2d, Object, int)
with the given colour and size.FontStyle
.FontStyle
.FontStyle
.FontStyle
.BlockingDroppingQueue
to make space for new elements within its
lifetime.FacialFeature
for EigenFaces.FacialFeatureExtractor
for producing EigenFaces.FaceRecogniser
based on Eigenfaces.FaceRecogniser
.IncrementalAnnotator
.CachingMultivariateGaussian
.DetectedFace
that represents the detection by an
ellipse.EllipticalDetectedFace
from the given parameters.AffineInterestPointFeatureCollector
to detect keypointMap
instances mapping token indexes to entities.YagoEntityCandidateFinderFactory.YagoEntityCandidateFinder
,
YagoEntityContextScorerFactory.YagoEntityContextScorer
and YagoEntityExactMatcherFactory.YagoEntityExactMatcher
.ImageProcessor
that performs histogram equalisation (projecting
the colours back into the image).Identifiable
is equal to a given Object
instance.StructuringElement.BOX
elementModel
that allows the model to be estimated from a
series of observations of both the independent and dependent variables.MixtureOfGaussians
from the given data.MixtureOfGaussians
from the given data.LinearRegression.estimate(List)
but using double arrays for efficiency.LinearRegression.estimate(List)
but using double arrays for efficiency.images
which [0] is first in the sequence and img2
which is
second in the sequence.x
value.Map
containing a particular metric value for each query.Evaluator
s are used to perform evaluations.HardAssigner
that assigns points to the closest
cluster based on the distance to the centroid.HardAssigner
that assigns points to the closest
cluster based on the distance to the centroid.HardAssigner
that assigns points to the closest cluster based on
the distance to the centroid.HardAssigner
that assigns points to the closest
cluster based on the distance to the centroid.HardAssigner
that assigns points to the closest
cluster based on the distance to the centroid.HardAssigner
that assigns points to the closest
cluster based on the distance to the centroid.HardAssigner
that assigns points to the closest
cluster based on the distance to the centroid.RunnableExperiment
.RunnableExperiment
s and automatically creating
and populating the context of the experiments.RecordInformationExtractor
to each key, printing out the results
in order to the provided PrintStream
WindowedHistogramExtractor
to extract (sub) histograms
from which to build the output.dst(x, y) = src(x + center.x (width(dst) 1) ⇤ 0.5, y + center.y (height(dst) 1) ⇤ 0.5)
.dst(x, y) = src(x + center.x (width(dst) 1) ⇤ 0.5, y + center.y (height(dst) 1) ⇤ 0.5)
.dst(x, y) = src(x + center.x (width(dst) 1) ⇤ 0.5, y + center.y (height(dst) 1) ⇤ 0.5)
.dst(x, y) = src(x + center.x (width(dst) 1) ⇤ 0.5, y + center.y (height(dst) 1) ⇤ 0.5)
.DoubleFV
.SequenceFile
.EigenImages
basis and a face
aligner.ScalingAligner
with its default resolutionFisherImages
basis and a
face aligner.IdentityAligner
ExtractorProperties
define the information a feature extractor needs
to extract a feature from something.PixelSet.crop(MBFImage, boolean)
com.restfb.FacebookClient
clientFImage
detector.FaceDetector
sFacialFeatureComparator
for FacialFeature
s that can
provide FeatureVector
s through the FeatureVectorProvider
interface.FVComparator
FacialFeature
that is just the pixel values
of a (possibly aligned) face detection.FacialFeatureExtractor
for producing FaceImageFeature
s.FacialFeature
that is built by concatenating each of the normalised
facial part patches from a detected face.FacialKeypoint
with an associated featureFacialFeatureExtractor
for producing FacialFeature
sFaceRecogniser
instances to
use in a CrossValidationBenchmark
.FaceRecognitionEngine
ties together the implementations of a
FaceDetector
and FaceRecogniser
, and provides a single
convenience API with which to interact with a face recognition system.FaceRecognitionEngine
with the given face detector
and recogniser.FaceSimilarityEngine
allows computation of the similarity
between faces in two images.FaceSimilarityEngine
from the
specified detector, extractor and comparator.FacialFeature
s
and producing a score.FacialFeature
from a DetectedFace
.FacialKeypoint
represents a keypoint on a face.FacialKeypoint
.NearestNeighbours
objects used in
assignment.null
then the whole StageTreeClassifier
fails.FImage
s as a @link{SinglebandImageProcessor}, with the kernel
itself formed from and @link{FImage}.FeatureVector
of the local featureBatchAnnotator
to behave like a
IncrementalAnnotator
by caching extracted features and then
performing training only when FeatureCachingIncrementalBatchAnnotator.annotate(Object)
is called.SpatialClusterer
that just produces a flat set of
centroids in the form of FeatureVector
s.ByteKMeans
used primarily as a convenience
function for reading.FeatureVector
s.SvdPrincipalComponentAnalysis
.PrincipalComponentAnalysis
object.FFastGaussianConvolve
to approximate blurring with a
Gaussian of standard deviation sigma.FGaussianConvolve
with a Gaussian of standard
deviation sigma.FGaussianConvolve
with a Gaussian of standard
deviation sigma.LocalFeatureList
backed by a file.InputStreamObjectReader
OptionHandler
that can provide a FileOutputStream
from
a file name.LocationFilter
to all the
elements of this list.ArrayList
.Predicate
.Iterable
.Iterator
.Predicate
.FImage
from an array of floating point values with the
given width and height.FImage
from an array of double values with the given
width and height.FImage
from an array of double values with the given
width and height.FImage
from an array of floating point values.FImage
of the given size.FImage
from an array of packed ARGB integers.FImage
from an array of packed ARGB integers using
the specified plane.ImageFromURL
for extracting FImage
sFImage
sInputStreamObjectReader
for reading FImage
s.FImageGradients.Mode.Signed
mode.FImageGradients.Mode
.OptionHandler
that can provide a FImage
from
a file name.BinaryReader
for CIFAR data that converts the encoded rgb pixel
values into an FImage
(by unweighted averaging).ImageRenderer
for FImage
images.IndexWriter.close()
ConnectedComponentLabeler.analyseImage(FImage)
followed by
ConnectedComponentLabeler.getComponents()
;GreyscaleConnectedComponentLabeler.analyseImage(FImage)
followed by
GreyscaleConnectedComponentLabeler.getComponents()
;OctaveInterestPointFinder
is considered the most basic.Keypoint
objectsKeypoint
objectsKeypoint
objectsKeypoint
objectsFile.listFiles(FilenameFilter)
find a file in the directory recursively (i.e.ByteBytePair
in ascending order.ByteDoublePair
in ascending order.ByteFloatPair
in ascending order.ByteIntPair
in ascending order.ByteLongPair
in ascending order.ByteShortPair
in ascending order.DoubleBytePair
in ascending order.DoubleDoublePair
in ascending order.DoubleFloatPair
in ascending order.DoubleIntPair
in ascending order.DoubleLongPair
in ascending order.DoubleShortPair
in ascending order.FloatBytePair
in ascending order.FloatDoublePair
in ascending order.FloatFloatPair
in ascending order.FloatIntPair
in ascending order.FloatLongPair
in ascending order.FloatShortPair
in ascending order.IntBytePair
in ascending order.IntDoublePair
in ascending order.IntFloatPair
in ascending order.IntIntPair
in ascending order.IntLongPair
in ascending order.IntShortPair
in ascending order.LongBytePair
in ascending order.LongDoublePair
in ascending order.LongFloatPair
in ascending order.LongIntPair
in ascending order.LongLongPair
in ascending order.LongShortPair
in ascending order.ShortBytePair
in ascending order.ShortDoublePair
in ascending order.ShortFloatPair
in ascending order.ShortIntPair
in ascending order.ShortLongPair
in ascending order.ShortShortPair
in ascending order.ByteBytePair
in descending order.ByteDoublePair
in descending order.ByteFloatPair
in descending order.ByteIntPair
in descending order.ByteLongPair
in descending order.ByteShortPair
in descending order.DoubleBytePair
in descending order.DoubleDoublePair
in descending order.DoubleFloatPair
in descending order.DoubleIntPair
in descending order.DoubleLongPair
in descending order.DoubleShortPair
in descending order.FloatBytePair
in descending order.FloatDoublePair
in descending order.FloatFloatPair
in descending order.FloatIntPair
in descending order.FloatLongPair
in descending order.FloatShortPair
in descending order.IntBytePair
in descending order.IntDoublePair
in descending order.IntFloatPair
in descending order.IntIntPair
in descending order.IntLongPair
in descending order.IntShortPair
in descending order.LongBytePair
in descending order.LongDoublePair
in descending order.LongFloatPair
in descending order.LongIntPair
in descending order.LongLongPair
in descending order.LongShortPair
in descending order.ShortBytePair
in descending order.ShortDoublePair
in descending order.ShortFloatPair
in descending order.ShortIntPair
in descending order.ShortLongPair
in descending order.ShortShortPair
in descending order.FirstBandDoGOctave
works like a DoGOctave
,
but with an MBFImage
, however, only the first band of
the MBFImage
is used to build the DoG pyramid.FirstBandDoGOctaveExtremaFinder
is an OctaveInterestPointFinder
that
can be applied to GaussianOctave
s.FacialFeature
for FisherFaces.FacialFeatureExtractor
for producing FisherFaces.FaceRecogniser
based on Fisherfaces.FaceRecogniser
.IncrementalAnnotator
.ActiveShapeModel.performIteration(Image, PointList)
until the
maximum number of iterations is exceeded, or the number of points that
moved less than 0.5 of their maximum distance in an iteration is less
than the target inlier percentage.SpatialBinningStrategy
that extracts normalised
HOG features in the style defined by Dalal and Triggs.ResizeProcessor
which speeds up the resize operation
between images of a given size to another fixed size by caching the contribution
calculationsFixedSizeBlockingChunkPartitioner
dynamically partitions data into
chunks of a fixed length.Queue
.Queue
and the given number
of items per chunk.FixedSizeChunkPartitioner
dynamically partitions data into chunks
of a fixed length.Iterable
.Iterable
and the given
number of items per chunk.HaarCascadeDetector
and the given
minimum search size.HaarCascadeDetector
and the given
minimum search size, and the given scale-factor for extracting the face
patch.SpatialBinningStrategy
very much like the FixedHOGStrategy
,
but with flexibly sized cells that grow/shrink such that the number of cells
in any given window is constant.FImage
.DataSource
backed by a 2D array of floats.ValueAnimator
capable of producing float arrays from
a set of potentially independent underlying ValueAnimator
s.ValueAnimator
sSpatialClusterer
that just produces a flat set of centroids.RandomisedHashFunction
for hashing float arrays.RandomisedHashFunction
for producing hash functions
seeded by random numbers.FloatKDTree.BBFMedianSplit
)FloatKeypoint
Keypoint
.FloatKMeans
used primarily as a convenience function for reading.SoftAssigner
that picks a fixed number of nearest neighbours.FloatLocalFeatureAdaptor
with the given
underlying featureFloatLocalFeatureAdaptor
with the given
underlying feature and normaliser.NearestNeighboursFactory
for producing
FloatNearestNeighboursExact
s.NearestNeighboursFactory
for producing
FloatNearestNeighboursKDTree
s.FloatProductQuantiser
with the given
nearest-neighbour assigners.ByteProductQuantiser
using
(Exact) K-Means.FImage
frames.FNormalLandmarkModel
is a landmark represented by the
surface normal line of a point (which is usually part of a
PointList
in an FImage
connected by PointListConnections
).FNormalLandmarkModel
sFontRenderer
with an associated FontStyle
.FontSimulator
creates an image during one of its
runs.AttributedString
s.SparseMatrix
instances and
therefore remain sparse.Iterable
data.Iterable
data.Operation
to each item in the stream.Operation
to each item in the stream.Operation
to each item in the stream.String.format(String, Object...)
at the appropriate levela
.a
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.a
.ReversableValueAnimator
that can wrap another
ReversableValueAnimator
to produce back and forth
looping behavior.ReversableValueAnimator
to
provide forward/backward looping behavior.FImage
convolution performed in the fourier domain.FImage
correlation performed using an FFT.FImage
s.FPatchLandmarkModel
is a landmark represented by the
local patch of pixels around of a point in an FImage
.FPatchLandmarkModel
sAudioProcessor
that provides frequency information.FrequencyAudioSource
.frexp
function to break
floating-point number into normalized fraction and power of 2.Matrix
from the Cognitive Foundry equivalentMatrix
from a matlab MLArray
HrsMinSecFrameTimecode.toString()
) back into a
timecode object.FScoreAnalysis
instanceImageAnalyser
that computes the X and Y image gradients using
Sobel filters.FStatisticalPixelProfileModel
is a statistical model of pixels
from an FImage
sampled along a line.FStatisticalPixelProfileModel
with the given
number of samples per line, and the given sampling strategy.MultivariateGaussian
with a full covariance
matrix.FundamentalModel
, automatically normalising data when
estimating the modelFundamentalModel
with optional automatic normalisation.ResidualCalculator
based on Sampson's geometric error.
Comparator
for comparing FValuePixel
s based on
the reversed natural order of their values.Comparator
for comparing FValuePixel
s based on
the natural order of their values.FeatureVector
types.FeatureExtractor
s that return objects that are
FeatureVectorProvider
s that is a FeatureExtractor
that
returns a FeatureVector
.GaussianMixtureModelEM
.EstimatableModel
that uses a
VectorNaiveBayesCategorizer
to associate vectors (actually double[])
with a category based on the naive bayes model.LTPWeighting
function.GeneralisedProcrustesAnalysis
with the given
parameters.GeneralisedProcrustesAnalysis
with the given
parameters.GeneralJSONTwitterRawText
extends GeneralJSONTwitter
to provide an object that can
read raw strings.FeatureExtractor
that is suitable for NaiveBayesAnnotator
.FImage
.TimeSeries.get(long, int, int)
with spans as 0TimeSeries.get(long, int, int)
but instead of createing the output
DATA instance, an existing data instance is handed which is filled.GroupedDataset
that represents the results from a
single category, returns a list of scored annotations for each group, for
question 1 (contains depication of category).GroupedDataset
that represents the results from a
single category, returns a list of scored annotations for each group, for
question 2 (is in category).ArrayBackedVideo
for the frames in this cache.AudioStream
which will return each of the sample buffers in turn.BinnedWindowedExtractor.analyseImage(FImage)
.AttributedString
.BlockingDroppingQueue
that is used as the
internal buffer.AbstractDenseSIFT.analyseImage(Image)
or AbstractDenseSIFT.analyseImage(Image, Rectangle)
in the form of a list of local features with byte vectors.AbstractDenseSIFT.analyseImage(Image)
or AbstractDenseSIFT.analyseImage(Image, Rectangle)
in the form of a list of local features with byte vectors.AbstractDenseSIFT.analyseImage(Image)
or PyramidDenseSIFT.analyseImage(Image, Rectangle)
in the form of a list of local features with byte vectors.AbstractDenseSIFT.analyseImage(Image)
or PyramidDenseSIFT.analyseImage(Image, Rectangle)
in the form of a list of local features with byte vectors.RGBRMSContrast.analyseImage(MBFImage)
RMSContrast.analyseImage(FImage)
WeberContrast.analyseImage(FImage)
Point2ds
.MultidimensionalByteFV.getIndex(int...)
MultidimensionalDoubleFV.getIndex(int...)
MultidimensionalFloatFV.getIndex(int...)
MultidimensionalIntFV.getIndex(int...)
MultidimensionalLongFV.getIndex(int...)
MultidimensionalShortFV.getIndex(int...)
ChessboardCornerFinder.analyseImage(FImage)
.AbstractDenseSIFT.analyseImage(Image)
or AbstractDenseSIFT.analyseImage(Image, Rectangle)
.#analyseImage(FImage)
or DenseSIFT.analyseImage(FImage, Rectangle)
.JasperPrint
detailing the result.String
detailing the result.LocalFeature
s extracted.PHOG.analyseImage(FImage)
.PHOG.analyseImage(FImage)
.FileSystem
corresponding to a Path
.AbstractDenseSIFT.analyseImage(Image)
or AbstractDenseSIFT.analyseImage(Image, Rectangle)
in the form of a list of local features with float vectors.AbstractDenseSIFT.analyseImage(Image)
or AbstractDenseSIFT.analyseImage(Image, Rectangle)
in the form of a list of local features with float vectors.AbstractDenseSIFT.analyseImage(Image)
or PyramidDenseSIFT.analyseImage(Image, Rectangle)
in the form of a list of local features with float vectors.AbstractDenseSIFT.analyseImage(Image)
or PyramidDenseSIFT.analyseImage(Image, Rectangle)
in the form of a list of local features with float vectors.FImageGradients
and call
FImageGradients.analyseImage(FImage)
with the image.FImageGradients
and call
FImageGradients.analyseImage(FImage)
with the image.FloatFV
.BingImageDataset.ImageDataSourceResponse
for the given
index.BingImageDataset.ImageDataSourceResponse
objects that back the
dataset.ImgurClient.ImgurTypeHash
instance calls
ImgurClient.getSingleImage(String)
, ImgurClient.getAlbumImages(String)
or
ImgurClient.getGalleryImages()
InOutToolOptions
.AbstractDenseSIFT.analyseImage(Image)
or PyramidDenseSIFT.analyseImage(Image, Rectangle)
.Line2d
with endpoints at the given x coordinates.PointList
.YagoEntityExactMatcherFactory.YagoEntityExactMatcher
from the default YagoEntity folder
path.YagoEntityExactMatcherFactory.YagoEntityExactMatcher
from the provided resource path.ConfusionMatrix
.Configuration
.Naturalness.analyseImage(MBFImage)
.NearestNeighbours
during
clustering.LocalTernaryPattern.analyseImage(FImage)
.EigenImages
object.Audio.getFormat()
for this class - returns the output format.InOutToolOptions
.BasicLocalBinaryPattern.analyseImage(FImage)
.ExtendedLocalBinaryPattern.analyseImage(FImage)
.Photo
object corresponding
to a particular image instance.Photo
objects.PhraseAnnotation.Phrase
based on the string.(x, y)
.(x, y)
.(x, y)
.(x, y)
.Pixel
s that are within this component.POSAnnotation.PartOfSpeech
from a string.LocalTernaryPattern.analyseImage(FImage)
.VideoCapture
representing the preferred device.Readability
instance from an html
string.Readability
instance from an html
string.ReferenceType
from a string.Keypoint
s from the input list, but with the
positions offset by the given amount.Gist.analyseImage(Image)
.FloatFV
.FilterBank.analyseImage(FImage)
.Stage
of the classifierByteBuffer
that can be used to create views of the
samples in the object.Saturation.analyseImage(MBFImage)
.SaturationVariation.analyseImage(MBFImage)
.WorldPlace
s.Sharpness.analyseImage(FImage)
.SharpnessVariation.analyseImage(FImage)
.FaceSimilarityEngine.performTest()
.Point2ds
.TokenAnnotation
(These should have POSAnnotation
)TokenAnnotation
sGetter
object has an implicit knowledge about the property it's setting,
and the instance of the option bean.JasperPrint
summarising the result.String
summarising the result.LocalTernaryPattern.analyseImage(FImage)
.Setter
s.a
, b
and c
.VLAD
aggregator instanceThreadPoolExecutor
.ThreadFactory
that produces daemon threads.GMMFromFeatures.DEFAULT_COMPONENTS
andWindowedHistogramExtractor
for efficiently
extracting gradient orientation histograms.GradientOrientationHistogramExtractor
with the
given number of bins.ScaleSpaceImageExtractorProperties
that holds edge
responses in the form of gradient orientations and magnitudes.VideoProcessor
that produces a slit-scan effect based on the time-map
in a greyscale image.Dataset
that is grouped into separate classes or groups.GroupedKFold
with the given number of folds.GroupedDataset
.GroupedDataset
into subsets for training,
validation and testing.GroupedUniformRandomisedSampler
with the given
percentage of instances to select.GroupedUniformRandomisedSampler
with the given
percentage of instances to select, using with with-replacement or
without-replacement sampling.GroupedUniformRandomisedSampler
with the given number
of instances to select.GroupedUniformRandomisedSampler
with the given number
of instances to select, using with with-replacement or
without-replacement sampling.GroupedDataset
.GrowingChunkPartitioner
dynamically partitions data into chunks.Iterable
.VideoCaptureException
with the specified detail
message.HaarCascadeDetector.BuiltInCascade.frontalface_default
cascade.HaarCascadeDetector.BuiltInCascade.frontalface_default
cascade and
the given minimum search window size.HadoopLocalFeaturesTool
.HadoopSiftLSHExtractor.MapperOut.hash
.HadoopSiftLSHExtractor.MapperOut.index
.HardAssigner
interface describes classes that assign a spatial
point to a single cluster.TimeBasedValueAnimator.isComplete()
Has the animator finished animating the value.aObject
is a possibly-null object field, and possibly an array.ImgurClient.ImgurType
is GALLERYhashCode
.HashComposition
s are HashFunction
s that compose the hash
codes generated by multiple hash functions applied to an object into a single
hash code for that object.HashFunction
s.ColourMap.Hot
by defaultRotatedRectangle.getHeight()
which is the height of the
regular bounding box)Normaliser
normalises vectors such that the Euclidean distance
between normalised vectors is equivalent to computing the similarity using
the Hellinger kernel on the un-normalised vectors.HierarchicalByteHardAssigner.ScoringScheme
determines how the distance
to a cluster is estimated from the hierarchy of k-means
generated clusters.HierarchicalByteKMeans
) is a simple
hierarchical version of ByteKMeans.HierarchicalByteKMeans
with the given parameters.HierarchicalByteKMeans
with the given parameters.HierarchicalByteKMeans
clustering operation.SoftAssigner
for gathering the clusters assigned
to a point from a hierarchical clustering.HierarchicalByteKMeansResult
instance.HierarchicalDoubleHardAssigner.ScoringScheme
determines how the distance
to a cluster is estimated from the hierarchy of k-means
generated clusters.HierarchicalDoubleKMeans
) is a simple
hierarchical version of DoubleKMeans.HierarchicalDoubleKMeans
with the given parameters.HierarchicalDoubleKMeans
with the given parameters.HierarchicalDoubleKMeans
clustering operation.SoftAssigner
for gathering the clusters assigned
to a point from a hierarchical clustering.HierarchicalDoubleKMeansResult
instance.HierarchicalFloatHardAssigner.ScoringScheme
determines how the distance
to a cluster is estimated from the hierarchy of k-means
generated clusters.HierarchicalFloatKMeans
) is a simple
hierarchical version of FloatKMeans.HierarchicalFloatKMeans
with the given parameters.HierarchicalFloatKMeans
with the given parameters.HierarchicalFloatKMeans
clustering operation.SoftAssigner
for gathering the clusters assigned
to a point from a hierarchical clustering.HierarchicalFloatKMeansResult
instance.HierarchicalIntHardAssigner.ScoringScheme
determines how the distance
to a cluster is estimated from the hierarchy of k-means
generated clusters.HierarchicalIntKMeans
) is a simple
hierarchical version of IntKMeans.HierarchicalIntKMeans
with the given parameters.HierarchicalIntKMeans
with the given parameters.HierarchicalIntKMeans
clustering operation.SoftAssigner
for gathering the clusters assigned
to a point from a hierarchical clustering.HierarchicalIntKMeansResult
instance.HierarchicalLongHardAssigner.ScoringScheme
determines how the distance
to a cluster is estimated from the hierarchy of k-means
generated clusters.HierarchicalLongKMeans
) is a simple
hierarchical version of LongKMeans.HierarchicalLongKMeans
with the given parameters.HierarchicalLongKMeans
with the given parameters.HierarchicalLongKMeans
clustering operation.SoftAssigner
for gathering the clusters assigned
to a point from a hierarchical clustering.HierarchicalLongKMeansResult
instance.HierarchicalShortHardAssigner.ScoringScheme
determines how the distance
to a cluster is estimated from the hierarchy of k-means
generated clusters.HierarchicalShortKMeans
) is a simple
hierarchical version of ShortKMeans.HierarchicalShortKMeans
with the given parameters.HierarchicalShortKMeans
with the given parameters.HierarchicalShortKMeans
clustering operation.SoftAssigner
for gathering the clusters assigned
to a point from a hierarchical clustering.HierarchicalShortKMeansResult
instance.ImageAnalyser
that processes an image and generates a
Histogram
.MBFPatchClassificationModel
that performs classification
based on the joint (colour) histogram of the patch by comparing the
patch histogram to a model histogram with a given comparison measure.MBFPixelClassificationModel
that classifies an individual pixel by
comparing it to a joint (colour) histogram.VideoShotDetector.setFPS(double)
before you process
any frames.HOG
with the 9 bins, using histogram
interpolation and unsigned gradients.HOG
with the given number of bins.FeatureExtractor
that wraps another
FeatureExtractor
and then applies the HomogeneousKernelMap
to
the output before returning the vector.HomogeneousKernelMap
HomogeneousKernelMap
.HomographyModel
that automatically normalises the data
given to HomographyModel.estimate(List)
to get a numerically stable estimate.HomographyModel
with optional automatic normalisation.HomographyModel
that automatically normalises the data
given to HomographyModel.estimate(List)
to get a numerically stable estimate.HomographyModel
with optional automatic normalisation.HorizontalIntensityDistribution
feature
extractor using the default of 10 equally spaced bins.HorizontalIntensityDistribution
feature
extractor using the given number of equally spaced bins.ImageAnalyser
.RedirectStrategy
that can deal with meta-refresh style
redirectionIdentifiable
is an object that has an associated identifier.Identifiable
that wraps another object.DetectedFace
object.DetectionFilter
; just outputs the input directly.FeatureExtractor
that wraps ImageAnalyser
s that
can provide FeatureVector
s through FeatureVectorProvider
.EstimatableModel
constructed between
an generic image and a probability map in the form of an FImage.FontSimulator.FontSimListener
interface.ImageAnalyser
that can provide interpolate pixel values using a
variety of interpolation approaches.ImgurClient.imgurURLtoHash(URL)
ImgurClient
instanceAnnotator
that can be trained/updated incrementally.byte[]
s.double[]
s.float[]
s.int[]
s.IncrementalSparseClusterer
which also has a notion of a lifetime.long[]
s.short[]
s.SparseMatrix
instances internally,
only forgetting rows once they have been clustered and are relatively stable.IndependentPair
represents a generic pair of objects of different
(independent) types.NumAnnotationsChooser
to determine how
many annotations are produced by calls to IndependentPriorRandomAnnotator.annotate(Object)
.RunnableExperiment
instance.IOUtils
DataSource
provides an indexed view of a subset of another
DataSource
.IndexedViewDataSource
with the given inner data
and indexes into the inner data.CitationAgent
at runtime.CitationAgent
at runtime.MultiTracker.initShape(Rectangle, Matrix, Matrix)
with the
rectangle of MultiTracker.TrackedFace.redetectedBounds
and the face shape and
the reference shape.GroupedListCache
InputMode
InputStream
.BlockingDroppingQueue
within its lifetime.FValuePixel.ReverseValueComparator
FValuePixel.ValueComparator
BatchAnnotator
to behave like a
IncrementalAnnotator
by caching instances and then performing
training only when InstanceCachingIncrementalBatchAnnotator.annotate(Object)
is called.DataSource
backed by a 2D array of ints.SpatialClusterer
that just produces a flat set of centroids.ValueAnimator
capable of producing int arrays from
a set of potentially independent underlying ValueAnimator
s.ValueAnimator
sInterestPointKeypoint
InterestPointData
from an interest point detector
InterestPointDetector
.InterestPointData
.null
location and default length featurenull
location and feature of the given
lengthInterestPointDetector
.TimeSeriesInterpolation.interpolate(DoubleTimeSeries, long[])
to return an
interpolation of the construction TimeSeries
between the times at
the required intervalTimeSeriesInterpolation.interpolate(DoubleTimeSeries, long[])
to return an
interpolation of the construction TimeSeries
from begin, for a
number of steps with a given delta between stepsTimeSeriesInterpolation.interpolate(DoubleTimeSeries,long[])
to return an
interpolation of the construction TimeSeries
from begin, until
end with a delta which means that there are splits time instancesInterpolatedBinnedWindowedExtractor
is an extension to a
BinnedWindowedExtractor
that performs soft assignment to the
histogram bins through linear interpolation.BasicOctaveExtremaFinder.DEFAULT_MAGNITUDE_THRESHOLD
for the
magnitude threshold, AbstractOctaveExtremaFinder.DEFAULT_EIGENVALUE_RATIO
for the Eigenvalue
ratio threshold and InterpolatingOctaveExtremaFinder.DEFAULT_INTERPOLATION_ITERATIONS
for the
number of iterations.AbstractOctaveExtremaFinder.DEFAULT_EIGENVALUE_RATIO
for the Eigenvalue ratio threshold and
InterpolatingOctaveExtremaFinder.DEFAULT_INTERPOLATION_ITERATIONS
for the number of iterations.p1
and p2
where the
return type is of polyClass
.p1
and p2
where the
return type is of PolyDefault
.Polygon.intersectionArea(Shape, int)
with 1 step per pixel
dimension.Polygon.intersectionArea(Shape, int)
with 1 step per pixel
dimension.RandomisedHashFunction
for hashing int arrays.RandomisedHashFunction
for producing hash functions
seeded by random numbers.IntKDTree.BBFMedianSplit
)IntKMeans
used primarily as a convenience function for reading.SoftAssigner
that picks a fixed number of nearest neighbours.Sketcher
that produces bit-string sketches encoded as int arrays.NearestNeighboursFactory
for producing
IntNearestNeighboursExact
s.NearestNeighboursFactory
for producing
IntNearestNeighboursKDTree
s.IntProductQuantiser
with the given
nearest-neighbour assigners.ByteProductQuantiser
using
(Exact) K-Means.a
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.Comparator
for Comparable
objects.PriorityQueue
implementation, where
objects that are higher (according to the provided Comparator
or the
natural order) come first.InvertedPriorityQueue
with the default initial capacity
(11) that orders its elements according to their inverted
natural ordering.InvertedPriorityQueue
with the specified initial
capacity that orders its elements according to the inverse of the
specified comparator.InvertedPriorityQueue
with the specified initial
capacity that orders its elements according to their inverse
natural ordering.InterestPointData
RetrievalEngine
.AnalysisResult
that is bascked by a SetRetrievalEvaluator
to
capture the results of a retrieval experiment.SetRetrievalEvaluator
result.ChessboardCornerFinder.analyseImage(FImage)
?BoundedPriorityQueue
full?Triangle.isInside(Point2d)
but counts being "on the line" as being
inside alsoUSMFStatus.reply_to
contains anything sensibleLocale
) is
supported by this tokeniser.Locale
) is supported by this tokeniser.AxesRenderer2D
to determine the position.AxesRenderer3D
to determine the position.IURLProtocolHandler
for jar resources.IURLProtocolHandlerFactory
for jar file resource urlsPattern
to match regexHardAssigner
that uses a ByteNearestNeighboursKDTree
to
generate approximately correct cluster assignments.HardAssigner
that uses a DoubleNearestNeighboursKDTree
to
generate approximately correct cluster assignments.HardAssigner
that uses a FloatNearestNeighboursKDTree
to
generate approximately correct cluster assignments.HardAssigner
that uses a IntNearestNeighboursKDTree
to
generate approximately correct cluster assignments.HardAssigner
that uses a LongNearestNeighboursKDTree
to
generate approximately correct cluster assignments.HardAssigner
that uses a ShortNearestNeighboursKDTree
to
generate approximately correct cluster assignments.KestrelServerSpec
ContextInsertor
that inserts an object at a specific key.Keypoint
Keypoint
.ListDataset
s.HaarCascadeDetector
to detect faces in
the image and then tracks them using the KLTTracker
.K
clusters.K
clusters.K
clusters.M
dimensions that will
create K
clusters.FontSimulator
.FontSimulator.FontSimListener
implementation that receives
each character as an image in some randomised font.AxesRenderer2D
to write labels onto the axes.LandmarkModel
models local image content and provides functionality
to move a point in an image to a nearby point with a lower cost than at the
initial point.LandmarkModelFactory
s are used to construct pre-configured
LandmarkModel
s on demand.LanguageDetector
LargeMarginDimensionalityReduction
is a technique to compress high
dimensional features into a lower-dimension representation using a learned
linear projection.nev
values/vectors.FilterBank
that provides the 16 raw kernels used in Laws texture
classification approach.MBFImage
s.ListDataset
.SWTTextDetector
.Linear
) or DenseLinear
depending on the density of the
features.ValueAnimator
that linearly animates a Byte value between two values.LinearByteValueAnimator
with the given
start and finish values, and the given duration in ticks
(number of calls to AbstractValueAnimator.nextValue()
.LinearByteValueAnimator
with the given
start and finish values, and the given duration in ticks
(number of calls to AbstractValueAnimator.nextValue()
.ValueAnimator
that linearly animates a Double value between two values.LinearDoubleValueAnimator
with the given
start and finish values, and the given duration in ticks
(number of calls to AbstractValueAnimator.nextValue()
.LinearDoubleValueAnimator
with the given
start and finish values, and the given duration in ticks
(number of calls to AbstractValueAnimator.nextValue()
.ValueAnimator
that linearly animates a Float value between two values.LinearFloatValueAnimator
with the given
start and finish values, and the given duration in ticks
(number of calls to AbstractValueAnimator.nextValue()
.LinearFloatValueAnimator
with the given
start and finish values, and the given duration in ticks
(number of calls to AbstractValueAnimator.nextValue()
.ValueAnimator
that linearly animates a Integer value between two values.LinearIntegerValueAnimator
with the given
start and finish values, and the given duration in ticks
(number of calls to AbstractValueAnimator.nextValue()
.LinearIntegerValueAnimator
with the given
start and finish values, and the given duration in ticks
(number of calls to AbstractValueAnimator.nextValue()
.ValueAnimator
that linearly animates a Long value between two values.LinearLongValueAnimator
with the given
start and finish values, and the given duration in ticks
(number of calls to AbstractValueAnimator.nextValue()
.LinearLongValueAnimator
with the given
start and finish values, and the given duration in ticks
(number of calls to AbstractValueAnimator.nextValue()
.LinearRegression
model, a time series is used as input to
calculate the coefficients of a linear regression such that value = b * time
+ c
This is the simplest kind of model that can be applied to a time seriesValueAnimator
that linearly animates a Short value between two values.LinearShortValueAnimator
with the given
start and finish values, and the given duration in ticks
(number of calls to AbstractValueAnimator.nextValue()
.LinearShortValueAnimator
with the given
start and finish values, and the given duration in ticks
(number of calls to AbstractValueAnimator.nextValue()
.Annotator
based on a set of linear SVMs (one per annotation).LinearSVMAnnotator
with the given extractor and
the specified negative class.LinearSVMAnnotator
with the given extractor.TimeBasedValueAnimator
that linearly animates a byte value between two
values over a given time.LinearTimeBasedByteValueAnimator
with the given
start and finish values, and the given duration in millseconds.TimeBasedValueAnimator
that linearly animates a double value between two
values over a given time.LinearTimeBasedDoubleValueAnimator
with the given
start and finish values, and the given duration in millseconds.TimeBasedValueAnimator
that linearly animates a float value between two
values over a given time.LinearTimeBasedFloatValueAnimator
with the given
start and finish values, and the given duration in millseconds.TimeBasedValueAnimator
that linearly animates a int value between two
values over a given time.LinearTimeBasedIntegerValueAnimator
with the given
start and finish values, and the given duration in millseconds.TimeBasedValueAnimator
that linearly animates a long value between two
values over a given time.LinearTimeBasedLongValueAnimator
with the given
start and finish values, and the given duration in millseconds.TimeBasedValueAnimator
that linearly animates a short value between two
values over a given time.LinearTimeBasedShortValueAnimator
with the given
start and finish values, and the given duration in millseconds.SWTTextDetector
.LineSampler
defines an interface for objects capable
of extracting information from pixels along a line in an image.ListBackedDataset
is a Dataset
backed by an ordered list of
items.ArrayList
as the backing store.ListDataset
is a Dataset
presented as an ordered list of
instances.SequenceFile
with the SequenceFileTool
.List
of Annotator.getAnnotations()
FaceRecognitionEngine
previously saved by
FaceRecognitionEngine.save(File)
.Cache.load(Object, Class, boolean)
with class as instance#getClass.PairMutualInformation.PAIR_STATS_FILE
LocalFeature
models a feature that has a Location
associated with it.LocalFeature
that internally holds
references to a FeatureVector
and Location
.Location
and
FeatureVector
.LocalFeature
s.DataSource
for the feature vector of one or more lists of
LocalFeature
s that use an ArrayFeatureVector
for the feature
vector.DoGSIFTEngine
.LocalFeatureMode
.LocalFeatureVectorProvider
models an object with both a
Location
and a feature vector associated with it.VideoShotDetector.setFPS(double)
before you process
any frames.ExtractorProperties
that holds the image being processed
and interest point locationFacialFeature
built from decomposing the face image into
(non-overlapping) blocks and building histograms of the
ExtendedLocalBinaryPattern
s for each block and then concatenating to
form the final feature.FacialFeatureExtractor
for building LocalLBPHistogram
s.Location
of the local featureFloatKeypoint
and another
FloatKeypoint
is the same.Location
objects.LocationProvider
marks classes that have an associated Location or
are capable of producing a Location
from their internal state.DataSource
backed by a 2D array of longs.ValueAnimator
capable of producing long arrays from
a set of potentially independent underlying ValueAnimator
s.ValueAnimator
sSpatialClusterer
that just produces a flat set of centroids.RandomisedHashFunction
for hashing long arrays.RandomisedHashFunction
for producing hash functions
seeded by random numbers.LongKDTree.BBFMedianSplit
)LongKMeans
used primarily as a convenience function for reading.SoftAssigner
that picks a fixed number of nearest neighbours.Sketcher
that produces bit-string sketches encoded as long arrays.NearestNeighboursFactory
for producing
LongNearestNeighboursExact
s.NearestNeighboursFactory
for producing
LongNearestNeighboursKDTree
s.LongProductQuantiser
with the given
nearest-neighbour assigners.ByteProductQuantiser
using
(Exact) K-Means.ForwardBackwardLoopingValueAnimator
from a ReversableValueAnimator
.LoopingValueAnimator
from a ValueAnimator
.ValueAnimator
that can wrap another
ValueAnimator
to produce looping behavior by
resetting when the animator has finished.ValueAnimator
to
provide looping behavior.SquareMissingLossFunction
HashModifier
that extracts the Least Significant Bit of the
underlying hash.LtpDtFeature
feature.FacialFeatureExtractor
for extracting LtpDtFeature
s.DepthOfFieldEstimator
and an alpha parameter of 0.9.Reference
annotations.PicSlurper
tool which reads from a stream which
this class constructs.ClassLoaderTransform.main(String[])
.LinearByteValueAnimator
s with the
range -max to +max and the given duration.LinearByteValueAnimator
s with the given start
and finish values, and the given duration.LinearDoubleValueAnimator
s with the
range -max to +max and the given duration.LinearDoubleValueAnimator
s with the given start
and finish values, and the given duration.LinearFloatValueAnimator
s with the
range -max to +max and the given duration.LinearFloatValueAnimator
s with the given start
and finish values, and the given duration.LinearIntegerValueAnimator
s with the
range -max to +max and the given duration.LinearIntegerValueAnimator
s with the given start
and finish values, and the given duration.LinearLongValueAnimator
s with the
range -max to +max and the given duration.LinearLongValueAnimator
s with the given start
and finish values, and the given duration.LinearShortValueAnimator
s with the
range -max to +max and the given duration.LinearShortValueAnimator
s with the given start
and finish values, and the given duration.RandomLinearByteValueAnimator
s with the
range -max to +max and the given duration.RandomLinearByteValueAnimator
s with the given start
and finish values, and the given duration.RandomLinearDoubleValueAnimator
s with the
range -max to +max and the given duration.RandomLinearDoubleValueAnimator
s with the given start
and finish values, and the given duration.RandomLinearFloatValueAnimator
s with the
range -max to +max and the given duration.RandomLinearFloatValueAnimator
s with the given start
and finish values, and the given duration.RandomLinearIntegerValueAnimator
s with the
range -max to +max and the given duration.RandomLinearIntegerValueAnimator
s with the given start
and finish values, and the given duration.RandomLinearLongValueAnimator
s with the
range -max to +max and the given duration.RandomLinearLongValueAnimator
s with the given start
and finish values, and the given duration.RandomLinearShortValueAnimator
s with the
range -max to +max and the given duration.RandomLinearShortValueAnimator
s with the given start
and finish values, and the given duration.Reference
from a BibTeXEntry
.Function
.Function
.MapBackedDataset
backed by a HashMap
.MapBackedDataset
instances from
Identifiable
sub-datasets.IdentityMapper
class.Matrix
instances from Adrian Kuhn's
library.Y = f(X)
MinMaxAnalyser.analyseImage(FImage)
MatlibMatrixUtils.minmaxmean(Matrix)
GlobalFeatureType.HISTOGRAM
) find the
maximum bin.ImageFromURL
for extracting
MBFImage
sMBFImage
sInputStreamObjectReader
for reading MBFImage
s.OptionHandler
that can provide a MBFImage
from
a file name.BinaryReader
for CIFAR data that converts the encoded rgb pixel
values into an MBFImage
.ImageRenderer
for MBFImage
images.MBFImage
frames.PatchClassificationModel
for
MBFImage
s.MBFImage
s.MBFStatisticalPixelProfileModel
is a statistical model of pixels
from an MBFImage
sampled along a line.MBFStatisticalPixelProfileModel
with the given
number of samples per line, and the given sampling strategy.MatlibMatrixUtils.minmaxmean(Matrix)
MeanAndCovariance
containing the mean vector and
covariance matrix of the given data (each row is a data point)MeanAndCovariance
containing the mean vector and
covariance matrix of the given data (each row is a data point)MeanAndCovariance
containing the mean vector and
covariance matrix of the given data (each row is a data point)ImageProcessor
that computes the mean of the image's pixels
and subtracts the mean from all pixels.USMFStatus
instances held in memory (backed by an ArrayList
.)ConnectedComponent
into
this object.SequenceFile
by the MetadataSequenceFileOutputFormat
.SequenceFile
that includes
additional metadata in the file header.MinMaxAnalyser.analyseImage(FImage)
MatlibMatrixUtils.minmaxmean(Matrix)
MinEpsilon
and
MaxIterations
to produce a predicate that stops (returns
true) as soon as either the minimum error is reached or the maximum number of
iterations is exceeded.MinMaxDoGSIFTEngine
with the default options.MinMaxDoGSIFTEngine
with the given options.Keypoint
extended to hold information on whether was detected at a
maxima or minima.MinMaxKeypoint
.MatlibMatrixUtils.min(Matrix)
, MatlibMatrixUtils.max(Matrix)
and
MatlibMatrixUtils.mean(Matrix)
A = D - A
A = A - B
A = A - D
Reference
BatchAnnotator
backed by a EstimatableModel
.DistanceCheck
that tests the distance against a
EstimatableModel
.y = Ax
.ImageRenderer
s that work on MultiBandImage
s.LocalFeatureMatcher
that only matches points that
are self similar with other points.MultiResolutionActiveShapeModel
from the
stack of provided ActiveShapeModel
s.Detector
.MultiThreadedDetector
with the given parameters.MultiThreadedDetector
with the given tree of stages
and scale factor.MultiThreadedDetector
with the given tree of
stages, and the default parameters for step sizes and scale factor.ClassFileTransformer
that applies one or more
ClassTransformer
s to a class before it is loaded.ClassTransformer
s.MultiviewSimilarityClusterer
clusters data that can be represented
as multiple similarity matrices.MusicalNoteFilterBank
to determine the power output of
the notes of a western scale over the frame.NaiveBayesAnnotator
with the given feature extractor
and mode of operation.NaiveBayesAnnotator
.NaiveBayesAnnotator
for sentiment analysis.GroupedDataset
.NearestNeighbours
objects for some given data.Predicate
.ClassificationEvaluator
, backed by the annotations
computed by this AnnotationEvaluator
, with the given
ClassificationAnalyser
.RetrievalEvaluator
, backed by the annotations computed
by this AnnotationEvaluator
, with the given
RetrievalAnalyser
.AnimatedVisualisationProvider
when
a new visualisation is available to drawn (but hasn't been
drawn yet).AnimatedVisualisationProvider
when
a new visualisation is available to drawn (but hasn't been
drawn yet).NMIAnalysis
instanceSimilarityMatrix
.SimilarityClusterer
around a datasetStrokeWidthTransform
for
display.FeatureVector
.NullModel
models a one-to-one mapping of data.Iterator
returned by
Iterable.iterator()
will perform.and byte
and double
and float
and int
and long
DistanceComparator
.DistanceComparator
.ObjectNearestNeighboursExact
over the provided
dataset with the given distance function.ObjectNearestNeighboursExact
over the provided
dataset with the given distance function.ObjectNearestNeighboursExact
with the given
distance function.NearestNeighboursFactory
for producing
ObjectNearestNeighboursExact
s.and short
AbstractOctaveLocalFeatureCollector
that collects Keypoint
s with the feature vector provided by the
given feature extractor.AbstractOctaveLocalFeatureCollector
that collects MinMaxKeypoint
s with the feature vector provided by the
given feature extractor.MapBackedDataset
from a number
of Identifiable
sub-datasets.MapBackedDataset
from a number
of Identifiable
sub-datasets.MapBackedDataset
from a number
of Identifiable
sub-datasets.MapBackedDataset
from a number
of Identifiable
sub-datasets.MapBackedDataset
from a number
of Identifiable
sub-datasets.MapBackedDataset
from a number
of Identifiable
sub-datasets.MapBackedDataset
from a number
of Identifiable
sub-datasets.SampleBuffer
s that each contain 1 second of audio.OpenCVGrouping
with the given parameters.OpenCVGrouping
with the given minimum support
number.OpenCVGrouping
with the default values of
OpenCVGrouping.DEFAULT_EPS
for the eps and OpenCVGrouping.DEFAULT_MINIMUM_SUPPORT
for
the support.TokenNameFinderModel
instnace to instanciate a NameFinderME
OpenNLPPersonAnnotator.PERSON_MODEL_PROP
ChunkerME
backed by a ChunkerModel
POSTaggerME
backed by a POSModel
SentenceDetectorME
backed by a SentenceModel
loaded from the
resource located at: OpenNLPSentenceAnnotator.SENTENCE_MODEL_PROP
Keypoint
Stage.outname()
System.err
stream from the running JVMOutputListener
modes for the command line toolSystem.out
stream from the running JVMReferencesToolOpts
.Image.padding(int, int, Object)
.Operation
to each item in the stream, making use
of multiple threads.Operation
to each item in the stream, making use
of multiple threads.CompassData
object.ByteArrayInputStream
and a DataInputStream
to read a byte[]TokenPairCount.identifier()
DisjointSetForest
by partitioning the given values
using the given Comparator
to determine equality of pairs of
values.Partitioner
partitions data into subsets that can
be processed in parallel.DisjointSetForest
which partitions the given values using the given Comparator
to
determine equality of pairs of values.ImageClassificationModel
based on the idea of determining the
probability of a class of a pixel given the local patch of pixels surrounding
the pixel in question.MeanCenter.patchMean(float[][], int, int, int, int)
but the width and height are taken from data[0].length and data.lengthPath2d
represents an arbitrary path between 2 points in a 2D space.PayloadCoordinate
.Coordinate
that has an associated
payload.AbstractTokenAnnotator
will annotate the
RawTextAnnotation
directly with TokenAnnotation
unless
there is already a SentenceAnnotation
.PhraseAnnotation.Phrase
label.PixelSet
.Pixel
s.AxesRenderer2D
to
provide the position of the object.AxesRenderer2D
to
provide the position of the object.AxesRenderer3D
to
provide the position of the object.AxesRenderer2D
to
provide the position of the object.AxesRenderer3D
to
provide the position of the object.AxesRenderer2D
to
provide the position of the object.A = A + B
A = A + B
A = A + d
A = A + D
A = D + A
KernelPerceptron
which works with
double array inputs and is binary.Point2dImpl
using the first two ordinates of a
Coordinate
.PointDistributionModel
from the given data with a
PointDistributionModel.NullConstraint
.PointDistributionModel
from the given data and
PointDistributionModel.Constraint
.PointDistributionModel
s so that generated models are plausible.GeometricObject2d
that is a set of
points in space.PointList
from pointsPointList
from pointsPointList
from the points, possibly copying the
points firstPointList
.PointList
PolygonUtils
is a Java version of the General Polygon Clipper
algorithm developed by Alan Murta (gpc@cs.man.ac.uk).HashComposition
that uses a polynomial function to combine the
individual hashes.Polyline
from pointsPolyline
from pointsPolyline
from the points, possibly copying the points
firstPolyline
from line segmentsParameterizedSparqlString
instancesRetrievalAnalyser
that computes the precision after N documents have
been retrieved (P@N).AnalysisResult
used with PrecisionAtN
to hold the P@N
precision after N documents have been retrieved.RDFSerializer
, this annotation should provide
the URI of the predicate to use for the field.Predicate
is used to apply a boolean test to a particular object to
see if it passes some criteria.Corpus.vocabularySize()
.Eigenvalues
perform the stages of spectral
clustering which involve the selection of the best eigen values and the
calling of an internal clustering algorithmPrincipalComponentAnalysis.ComponentSelector
that selects a subset of the principal
components such that all remaining components have a cumulative energy
less than the given value.PrincipalComponentAnalysis.ComponentSelector
that selects the n-best components.PrincipalComponentAnalysis.ComponentSelector
that selects a subset of the principal
components such that all remaining components have a certain percentage
cumulative energy of the total.KernelProcessor
and return new
image containing the result.KernelProcessor
and return new
image containing the result.GridProcessor
and return new
image containing the result.ImageProcessor
and return new image
containing the result.KernelProcessor
and return new
image containing the result.KernelProcessor
and return new
image containing the result.PixelProcessor
and return a new
image containing the result.Processor
and return new image
containing the result.SinglebandImageProcessor
returning a
new image.SinglebandImageProcessor
for
every band.ConnectedComponentProcessor
.ConnectedComponentProcessor
and returns a new component
containing the result.ConnectedComponent
.ConnectedComponent
.ConnectedComponent
.ConnectedComponent
.ConnectedComponent
.SinglebandImageProcessor
returning a
new image.KernelProcessor
and return new
image containing the result.KernelProcessor
and return new
image containing the result.SinglebandImageProcessor
returning a
new image.SimilarityMatrix
, making changes inplace.VideoProcessor.process(Video)
with the
current video (for chainable processors).VideoAnalyser.analyseFrame(Image)
on the input image.patch
) and returns
a single pixel value for that element.FSobel
analyser to generate the gradients.Inpainter.setMask(int,int,Collection)
, Inpainter.setMask(FImage)
or
Inpainter.setMask(int,int,PixelSet...)
PixelProcessor
side-affecting
this image.Processor
side-affecting this
image.ImageProcessor
side-affecting
this image.KernelProcessor
side-affecting
this image.KernelProcessor
side-affecting
this image.PixelProcessor
side-affecting
this image.SinglebandImageProcessor
storing the
result in this processable image.SinglebandImageProcessor
for
every band.ConnectedComponentProcessor
.SinglebandImageProcessor
storing the
result in this processable image.SinglebandImageProcessor
storing the
result in this processable image.PixelProcessor
only affecting
those pixels where the mask is non-zero.PixelProcessor
, only affecting
those pixels where the mask is non-zero.JavaProcess.ProcessOptions
that inherits the classpath of
the current JVM and will runs the main method of the given class.JavaProcess.ProcessOptions
that will run the given jar
file.JavaProcess.ProcessOptions
with the given classpath and
main class.Processor
is the super (marker) interface for
ImageProcessor
s, KernelProcessor
s and PixelProcessor
s.Processor
s.SiteSpecificConsumer
instances loaded into
SiteSpecificURLExtractor.siteSpecific
.SiteSpecificConsumer
instances loaded into
SiteSpecificURLExtractor.siteSpecific
.PointList
s.ProcrustesAnalysis
with the given
reference shape.ProcrustesAnalysis
with the given
reference shape.ProxyOptionHandler
allows options to have associated options.FImage
s.PyramidSpatialAggregator
performs spatial pooling of local features
by grouping the local features into fixed-size spatial blocks within a
pyramid, and applying a VectorAggregator
(i.e.QuadtreeSampler
provides an easy way of extracting sample patches
from an image or other domain in both spatial and scale directions.SpatialBinningStrategy
that extracts histograms from regions
defined by a fixed depth quadtree overlayed over the sampling region and
concatenates them together.QuantisedLocalFeature
with a location described by an
AffineSimulationKeypoint.AffineSimulationKeypointLocation
.QuantisedAffineSimulationKeypoint
, located at
the origin with an id of 0.QuantisedAffineSimulationKeypoint
, located at the
origin with the given id.QuantisedAffineSimulationKeypoint
, located at the
given position and id.QuantisedKeypoint
, located at the origin with
an id of 0.QuantisedKeypoint
, located at the given position with
an id of 0.QuantisedKeypoint
, located at the given position and
id.Directory
index and an Analyzer
allow for
searches of particular fields.RandomIndexAnalysis
instanceClusterAnalyser
as corrected by
a Random baseline.TestRandomBaselineClusterAnalyser
which wraps the
baseline result and result of an AnalysisResultAnalysisResult
which can offer some score and thus be compared to
a random baselineRandomDecision
nodes used for constructing a string of bits which represent a cluster
point for a single data pointRandomisedHashFunction
s.ValueAnimator
that continuously animates between
randomly selected values in a range.RandomLinearByteValueAnimator
with the given
range and duration for each sub-animation.RandomLinearByteValueAnimator
with the given
range, duration for each sub-animation and fixed initial value.RandomLinearByteValueAnimator
with the given
range and duration for each sub-animation.RandomLinearByteValueAnimator
with the given
range, duration for each sub-animation and fixed initial value.ValueAnimator
that continuously animates between
randomly selected values in a range.RandomLinearDoubleValueAnimator
with the given
range and duration for each sub-animation.RandomLinearDoubleValueAnimator
with the given
range, duration for each sub-animation and fixed initial value.RandomLinearDoubleValueAnimator
with the given
range and duration for each sub-animation.RandomLinearDoubleValueAnimator
with the given
range, duration for each sub-animation and fixed initial value.ValueAnimator
that continuously animates between
randomly selected values in a range.RandomLinearFloatValueAnimator
with the given
range and duration for each sub-animation.RandomLinearFloatValueAnimator
with the given
range, duration for each sub-animation and fixed initial value.RandomLinearFloatValueAnimator
with the given
range and duration for each sub-animation.RandomLinearFloatValueAnimator
with the given
range, duration for each sub-animation and fixed initial value.ValueAnimator
that continuously animates between
randomly selected values in a range.RandomLinearIntegerValueAnimator
with the given
range and duration for each sub-animation.RandomLinearIntegerValueAnimator
with the given
range, duration for each sub-animation and fixed initial value.RandomLinearIntegerValueAnimator
with the given
range and duration for each sub-animation.RandomLinearIntegerValueAnimator
with the given
range, duration for each sub-animation and fixed initial value.ValueAnimator
that continuously animates between
randomly selected values in a range.RandomLinearLongValueAnimator
with the given
range and duration for each sub-animation.RandomLinearLongValueAnimator
with the given
range, duration for each sub-animation and fixed initial value.RandomLinearLongValueAnimator
with the given
range and duration for each sub-animation.RandomLinearLongValueAnimator
with the given
range, duration for each sub-animation and fixed initial value.ValueAnimator
that continuously animates between
randomly selected values in a range.RandomLinearShortValueAnimator
with the given
range and duration for each sub-animation.RandomLinearShortValueAnimator
with the given
range, duration for each sub-animation and fixed initial value.RandomLinearShortValueAnimator
with the given
range and duration for each sub-animation.RandomLinearShortValueAnimator
with the given
range, duration for each sub-animation and fixed initial value.DataSource
provides randomly sampled view over another
DataSource
.DataSource
such that it has
requestedSize items.DataSource
such that it has the
given proportion of items from the original.RandomSetByteClusterer
however it is
guaranteed that the same training vector will not be sampled more than once.RandomSetDoubleClusterer
however it is
guaranteed that the same training vector will not be sampled more than once.RandomSetFloatClusterer
however it is
guaranteed that the same training vector will not be sampled more than once.RandomSetIntClusterer
however it is
guaranteed that the same training vector will not be sampled more than once.RandomSetLongClusterer
however it is
guaranteed that the same training vector will not be sampled more than once.RandomSetShortClusterer
however it is
guaranteed that the same training vector will not be sampled more than once.SparseColumnMatrix
SparseRowMatrix
RangePartitioner
partitions data of a known size into a predefined
number of equally sized partitions.List
of data and the given number of partitions.Collection
of data and the given number of
partitions.List
of data and the number of partitions equal
to the number of hardware threads.Collection
of data and the number of partitions
equal to the number of hardware threads.TextPipeAnnotation
so that it may be
annotated.GeneralJSON
can be translated to JSON very
easily The same analysis cannot be easily translated to RDF so this class
must be registered in GeneralJSONRDF's map to do so.RDFSerializer
to serialise objects.Predicate
annotation does not exist.MFCheck
VLADIndexerData
object to the given file created with the
VLADIndexerData.write(File)
method.VLADIndexerData
object to the given stream created with
the VLADIndexerData.write(OutputStream)
method.BufferedReader
for
a given file.IOUtils.write(Object, DataOutput)
.List
that is readable.GroupedDataset
s in which each instance is read with an
InputStreamObjectReader
.InputStreamObjectReader
.List
that is readable.ListDataset
s in which each instance is read with an
InputStreamObjectReader
.ObjectReader
.Map
that is readable.FImage
from the given file.FImage
from the given input stream.FImage
from the given URL.FImage
from a DataInputScanner
IOUtils.writeToFile(Object, File)
.MBFImage
from the given file.MBFImage
from the given input stream.MBFImage
from the given URL.MBFImage
from the given file.MBFImage
from the given input stream.MBFImage
from the given URL.DataInput
.TimeFrequencyHolder
from a Path
.HttpURLConnection
to the URL
as an array of
bytes.HttpURLConnection
to the URL
as an array of
bytes.URL
as a
ByteArrayInputStream
(i.e.URL
as a
ByteArrayInputStream
(i.e.URL
as a
ByteArrayInputStream
(i.e.URL
as an array of bytes.URL
as an array of bytes.InputStream
to the contents referenced by the URL
.InputStream
to the contents referenced by the URL
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.a
.TimeFrequencyHolder.TimeFrequency
reset TimeFrequencyHolder.TimeFrequency
cumulativeFrequency = TimeFrequencyHolder.TimeFrequency
periodFrequency and then go
through each in key-value order and use
TimeFrequency#combine(TimeFrequency)
to calculate a cumulative
countString
) from a record (key-value pair)
in a SequenceFile.RectangleSampler
provides an easy way to generate a sliding window
of rectangle over an image or other domain.IdentityReducer
class.ReferenceFormatter
defines an interface for objects capable of
converting Reference
and References
objects into
String
s.Reference
annotations.Processor
implementation that is capable of finding Reference
and References
annotations and generating lists which are then
written.ClassTransformer
that dynamically augments classes and methods
annotated with Reference
or References
annotations to
register the annotations with a global listener if the class is constructed,
or the method is invoked.PathFilter
based on matching regular expressions.RegionMode
instances can provide Neighbours of the n'th data
point given all the data pointsBlockingDroppingBufferedStream.register(Object)
it with the stream.BlockingDroppingBufferedStream.register(Object)
it with the stream.BlockingDroppingBufferedStream.register(Object)
it with the stream.L1L2Regulariser
out
out
.ImageProcessor
and associated static utility methods for transforming
an image based on the pixel positions given by a distortion map:
destination(x,y) = source(mapx(x,y), mapy(x,y))
.ImageInterpolation.InterpolationType.BILINEAR
interpolation.ComponentStackMergeListener
from the listeners
list.AudioMixer.MixEventListener
from this mixer.SerialDataListener
from the listener listImageRenderer
implementations to (optionally)
use when drawing.AttributedString
to the image starting at (x,y).PixelAnalyser
.StatusConsumer.urlToImage(URL)
to turn the url into a list of
images and write the images into the output location using the names
"image_N.png"RestrictedAnnotator
interface describes annotators
that can predict annotations based on an external context
that restricts what annotations are allowed.ByteCentroidsResult
for this nodeDoubleCentroidsResult
for this nodeFloatCentroidsResult
for this nodeIntCentroidsResult
for this nodeLongCentroidsResult
for this nodeShortCentroidsResult
for this nodeResultAggregator
aggregates multiple results
into a single AnalysisResult
.RetrievalAnalyser
is used to analyse the raw search results from a
RetrievalEngine
in the context of a RetrievalEvaluator
and to
produce an AnalysisResult
describing the performance of the
RetrievalEngine
.Evaluator
for the evaluation of retrieval
experiments using the Cranfield methodology.RetrievalEvaluator
with a search engine, a set of
queries to perform, relevant documents for each query, and a
RetrievalAnalyser
to analyse the results.RetrievalEvaluator
with a search engine, relevant
documents for each query, and a RetrievalAnalyser
to analyse the
results.RetrievalEvaluator
with the given ranked results
lists and sets of relevant documents for each query, and a
RetrievalAnalyser
to analyse the results.ValueAnimator
s that can be reversed.RigidTransformModel3d
LMedS
algorithm with the given expected
outlier percentageRANSAC
algorithm with the given options.LMedS
algorithm with the given expected
outlier percentageRANSAC
algorithm with the given options.LMedS
algorithm with the given expected
outlier percentageRANSAC
algorithm with the given options.LMedS
algorithm with the given expected
outlier percentageRANSAC
algorithm with the given options.LMedS
algorithm with the given expected
outlier percentageRANSAC
algorithm with the given options.ClassificationAnalyser
capable of producing
a Receiver Operating Characteristic curve and associated
statistics.AnalysisResult
representing a set of ROC curves and associated
statistics.YehSaliency
estimator.PointList
about the given origin with the given angle
(in radians)PointList
about (0,0) with the given angle (in
radians)Rectangle
about the given pivot with the given angle
(in radians)Rectangle
rotated about its centroidObjectDetector
that wraps another ObjectDetector
and
performs rotation simulations on the images it passes to the internal
detector.Math.round(double)
each value of the matrixTwitterSearchDataset
to support multiple queries.Iterable
over the rowsRuleOfThirds
with the default settings for the
YehSaliency
algorithm.RuleOfThirds
with the given values for the
YehSaliency
algorithm.Loader
that is
configured to apply the given transform(s), and then run the main method.Loader
,
load it in a newly created Loader
that is configured to apply the
given transform(s), run the main method and return true.ExperimentRunner
.main
main method of the given class in a new JVM.main
main method of the given class in a new JVM.main
main method of the given class in a new JVM.main
main method of the given class in a new JVM.JavaProcess.ProcessOptions
in a
separate JVM and wait for it to exit.nItems
items from the collection set by
CollectionSampler.setCollection(Collection)
, returning a new collection with the
given samples.SampleBuffer
for 16-bit sample chunks.SampleBuffer
for 8 bit sample chunks.SampleBuffer
s from AudioFormat
s.AudioGrabber
when a new sample chunk is available
for processing.SandeepFaceDetector
with the default skin-tone
model.WindowedHistogramExtractor
with the primary
purpose of of producing efficient access to histograms of arbitrary windows
of the image.FaceRecognitionEngine
to a file, including all the
internal state of the recogniser, etc.PointList
by the given amount about (0,0).PointList
by the given amount about the given point.this
.PointList
about its centre of gravity.ScaleSpaceImageExtractorProperties
.LocalImageExtractorProperties
that additionally holds the
interest point location scale.Location
in scale-space.Point2d
in scale-space.PointList
only in the x-direction by the given amount
about (0,0).PointList
by the given amount about (0,0).PointList
only in the y-direction by the given amount
about (0,0).DetectedFace
to a predefined size.ByteBytePair
in ascending order.ByteDoublePair
in ascending order.ByteFloatPair
in ascending order.ByteIntPair
in ascending order.ByteLongPair
in ascending order.ByteShortPair
in ascending order.DoubleBytePair
in ascending order.DoubleDoublePair
in ascending order.DoubleFloatPair
in ascending order.DoubleIntPair
in ascending order.DoubleLongPair
in ascending order.DoubleShortPair
in ascending order.FloatBytePair
in ascending order.FloatDoublePair
in ascending order.FloatFloatPair
in ascending order.FloatIntPair
in ascending order.FloatLongPair
in ascending order.FloatShortPair
in ascending order.IntBytePair
in ascending order.IntDoublePair
in ascending order.IntFloatPair
in ascending order.IntIntPair
in ascending order.IntLongPair
in ascending order.IntShortPair
in ascending order.LongBytePair
in ascending order.LongDoublePair
in ascending order.LongFloatPair
in ascending order.LongIntPair
in ascending order.LongLongPair
in ascending order.LongShortPair
in ascending order.ObjectBytePair
in ascending order.ObjectDoublePair
in ascending order.ObjectFloatPair
in ascending order.ObjectIntPair
in ascending order.ObjectLongPair
in ascending order.ObjectShortPair
in ascending order.ShortBytePair
in ascending order.ShortDoublePair
in ascending order.ShortFloatPair
in ascending order.ShortIntPair
in ascending order.ShortLongPair
in ascending order.ShortShortPair
in ascending order.ByteBytePair
in descending order.ByteDoublePair
in descending order.ByteFloatPair
in descending order.ByteIntPair
in descending order.ByteLongPair
in descending order.ByteShortPair
in descending order.DoubleBytePair
in descending order.DoubleDoublePair
in descending order.DoubleFloatPair
in descending order.DoubleIntPair
in descending order.DoubleLongPair
in descending order.DoubleShortPair
in descending order.FloatBytePair
in descending order.FloatDoublePair
in descending order.FloatFloatPair
in descending order.FloatIntPair
in descending order.FloatLongPair
in descending order.FloatShortPair
in descending order.IntBytePair
in descending order.IntDoublePair
in descending order.IntFloatPair
in descending order.IntIntPair
in descending order.IntLongPair
in descending order.IntShortPair
in descending order.LongBytePair
in descending order.LongDoublePair
in descending order.LongFloatPair
in descending order.LongIntPair
in descending order.LongLongPair
in descending order.LongShortPair
in descending order.ObjectBytePair
in descending order.ObjectDoublePair
in descending order.ObjectFloatPair
in descending order.ObjectIntPair
in descending order.ObjectLongPair
in descending order.ObjectShortPair
in descending order.ShortBytePair
in descending order.ShortDoublePair
in descending order.ShortFloatPair
in descending order.ShortIntPair
in descending order.ShortLongPair
in descending order.ShortShortPair
in descending order.hashCode
, to which is added contributions
from fields.EntityExtractionResourceBuilder
.ConnectedComponent
s.PixelSet
s (or subclasses thereof).PrincipalComponentAnalysis.ComponentSelector
.SentenceAnnotation
is an extension of RawTextAnnotation
as
its function is to encapsulate a substring of raw text.FeatureSelect
as a
ByteKMeans
instanceSequenceFileTool
is a commandline tool for creating, extracting and
inspecting Hadoop SequenceFile
s.Predicate
.DataUnitsTransformer
given here
should be able to do that.DataUnitsTransformer
given here
should be able to do that.DataUnitsTransformer
given here
should be able to do that.FloatKeypoint
Keypoint
Configuration
.NearestNeighbours
during
clustering.ParameterizedSparqlString
nullPrincipalComponentAnalysis.ComponentSelector
.(x,y)
to the given value.(x,y)
to the given value.(x,y)
to the given value.(x,y)
to the given value.(x,y)
to the given value.ParameterizedSparqlString
literalParameterizedSparqlString
literalParameterizedSparqlString
literalParameterizedSparqlString
literalRandom
object is seeded and used to choose
random indecies and thresholds.SparseVector.size()
Use with
care.ShapeModelDataset
instances.DataSource
backed by a 2D array of shorts.ValueAnimator
capable of producing short arrays from
a set of potentially independent underlying ValueAnimator
s.ValueAnimator
sSpatialClusterer
that just produces a flat set of centroids.RandomisedHashFunction
for hashing short arrays.RandomisedHashFunction
for producing hash functions
seeded by random numbers.ShortKDTree.BBFMedianSplit
)ShortKMeans
used primarily as a convenience function for reading.SoftAssigner
that picks a fixed number of nearest neighbours.Sketcher
that produces bit-string sketches encoded as short arrays.NearestNeighboursFactory
for producing
ShortNearestNeighboursExact
s.NearestNeighboursFactory
for producing
ShortNearestNeighboursKDTree
s.ShortProductQuantiser
with the given
nearest-neighbour assigners.ByteProductQuantiser
using
(Exact) K-Means.x
, assuming that it is 1D complex
array.x
, assuming that it is 2D complex
array.x
, assuming that it is 3D complex
array.x
.x
, assuming that it is 3D complex
array.x
, assuming that it is 1D real array.x
, assuming that it is 2D real array.x
, assuming that it is 3D real array.x
.SIFTFeatureProvider
with the default parameters.SIFTFeatureProvider
with the provided options.SIFTFeatureProvider
with the provided options.LocalFeature
based on the .siftgeo format
developed by Krystian Mikolajczyk for his tools.SIFTGeoKeypoint
.SimilarityClusterer
clusters data that can be represented as a similarity
matrix.DBSCAN
using a SparseMatrix
of similaritiesSimilarityMatrix
.SpatialBinningStrategy
that extracts histograms from a number of
equally-sized, non-overlapping within the sample region and concatenates them
together.InetSocketAddress.InetSocketAddress(String, int)
instanceClient
instance using ServiceFactory
from a
ClientBuilder
SimpleMEClusterAnalyser
.AbstractTwitterPreprocessingToolOptions
from the
HadoopTwitterPreprocessingTool.ARGS_KEY
variable (once per in memory
mapper) and uses these to preprocess tweets.SingleBandImage
s.Image
s that are processable by
SinglebandImageProcessor
s.MBFPixelClassificationModel
that classifies an individual pixel by
comparing it to a CachingMultivariateGaussian
.SiteSpecificConsumer
s to the
input.Slideshow
.Slide
s.VideoProcessor
that produces a slit-scan effect.SoftAssigner
interface describes classes that assign a spatial
point to multiple clusters, possibly with weighting.SoftAssigner
with an associated codebook.Ax = 0
, returning the vector x as an array.Ax = 0
, returning the vector x as an array.Ax = 0
, returning the vector x as an array.SparseBinSearchByteArray
wrapper which works around
an existing array of keys and valuesSparseBinSearchDoubleArray
wrapper which works around
an existing array of keys and valuesSparseBinSearchFloatArray
wrapper which works around
an existing array of keys and valuesSparseBinSearchIntArray
wrapper which works around
an existing array of keys and valuesSparseBinSearchLongArray
wrapper which works around
an existing array of keys and valuesSparseBinSearchShortArray
wrapper which works around
an existing array of keys and valuesSparseByteArray
s
at the same index.SparseByteArray
, consisting of
an index and value.SparseDoubleArray
s
at the same index.SparseDoubleArray
, consisting of
an index and value.SparseFloatArray
s
at the same index.SparseFloatArray
, consisting of
an index and value.SparseByteArray
implementation based on a
TIntByteHashMap
.SparseDoubleArray
implementation based on a
TIntDoubleHashMap
.SparseFloatArray
implementation based on a
TIntFloatHashMap
.SparseIntArray
implementation based on a
TIntIntHashMap
.SparseLongArray
implementation based on a
TIntLongHashMap
.SparseShortArray
implementation based on a
TIntShortHashMap
.SparseIntArray
s
at the same index.SparseIntArray
, consisting of
an index and value.SparseLongArray
s
at the same index.SparseLongArray
, consisting of
an index and value.SparseShortArray
s
at the same index.SparseShortArray
, consisting of
an index and value.SparseVector
to a
SparseDoubleArray
.WindowedHistogramExtractor
.SpatialClusterer
clusters data that can be represented as points in
a space.SpatialClusterer
.Location
in 2d-space.EigenChooser
is set to an
ChangeDetectingEigenChooser
which looks for a 100x gap between
eigen vectors to select number of clusters.EigenChooser
is set to an
ChangeDetectingEigenChooser
which looks for a 100x gap between
eigen vectors to select number of clusters.EigenChooser
is set to an
ChangeDetectingEigenChooser
which looks for a 100x gap between
eigen vectors to select number of clusters.IndexClusters
which also hold the eigenvector/value pairs which created themSpatialClusterer
that just produces a flat set of
centroids.MultivariateGaussian
(diagonal
covariance matrix with equal values).StageTreeClassifier
with the given parameters.StreamJSONStatusList.ReadableWritableJSON
and
output image files.HttpUtils.MetaRefreshRedirectStrategy
which disallows
all redirects and instead remembers a redirect for use later on.LocalTweetSpout
fed from the stdinDirectedGraph
instances from StormTopology
instancesComponentCommon
as A, B, C...StormTopologyMode
uses a StormSubmitter
to submit a
StormTopology
constructed using
StormToolOptions.constructTopology()
.StratifiedGroupedKFold
with the given target number
of folds, K.StratifiedGroupedUniformRandomisedSampler
with the given percentage of instances to select.StratifiedGroupedUniformRandomisedSampler
with the given percentage of instances to select, using with
with-replacement or without-replacement sampling.StratifiedGroupedUniformRandomisedSampler
with the given number of instances to select.StratifiedGroupedUniformRandomisedSampler
with the given number of instances to select, using with with-replacement
or without-replacement sampling.FeatureFile
backed by a stream or file.LocalFeature
s backed by an input stream.USMFStatus
instances using various methods.StringMurmurHashFunction
with the default seed.StringMurmurHashFunction
with the given seed.HashFunctionFactory
for producing StringMurmurHashFunction
s
with randomly assigned seeds.MersenneTwister
seeded with the current time.NGramGenerator.NGramGenerator(Class)
with String#classFImage
from this image returning a new image
containing the result.s
from all elements in a1
overwriting
the array.s
from all elements in a1
overwriting
the array.s
from all elements in a1
overwriting
the array.s
from all elements in a1
overwriting
the array.s
from all elements in a1
overwriting
the array.s
from all elements in a1
overwriting
the array.FImage
from this image.null
then the whole StageTreeClassifier
passes.WordDFIDF
instances all come
from the same period of time and therefore have the same total number of
tweets and total number of word instances across time (i.e.SvdPrincipalComponentAnalysis
that will extract all
the eigenvectors.SvdPrincipalComponentAnalysis
that will extract the n
best eigenvectors.ImageRenderer
for FImage
images.ImageRenderer
implementations to (optionally)
use when drawing.IndependentPair
from this one with the elements
swappedSWTTextDetector
with the default parameters.SWTTextDetector
with the given parameters.SWTTextDetector
.aij = aji
for all elements.TemplateMatcher.Mode.CORRELATION
FImage
s.Job
s that injest and output Text
keys and BytesWritable
values.SequenceFileUtility
for
SequenceFile
s with Text
keys and BytesWritable
values.TextLineIterable.Provider
for gzipped text filesTextLineIterable
TextPipeAnnotation
are annotations and
annotatable.ThinSvdPrincipalComponentAnalysis
that
will extract the n best eigenvectors.DistanceCheck
that tests the distance against a
fixed threshold.KernelPerceptron
which works with
double array inputs and is binary.Segmenter
s by
applying the thresholding operation and then gathering the pixel sets
belonging to each segment.KestrelServerSpec
in a round
robin fasion.ClassFileTransformer
that dynamically augments classes and methods
annotated with Time
annotations in order to register and collect the
method timing information.ReadWriteableBinary
TLongObjectHashMap
TimelineObject
s.R = D .
R = A .
TimeSeries
in place.TimeSeries
classes to get
arrays of spans of timeDefaultTokenFactory
to load the default api tokenEllipseUtilities.ellipseFromCovariance(float, float, Matrix, float)
and the Polygon.calculateSecondMomentCentralised()
return the Ellipse
best fitting the shape of this polygon.IdentifiableObject
s.TokenAnnotation
represents a single token generated by a tokeniser.TokenPairUnaryCount.TokenPairUnaryCount(String, String, long, long, long)
using the values from
the TokenPairCount
instanceMLArray
to a Matrix
Comparator
used in
constructing the queue.Comparator
used in constructing the queue.TopologyModeOption
believes it is donePolygon
representation
by performing a 4-connected boundary trace and converting the resulting
pixels into vertices.String.format(String, Object...)
) to format
each pixel value.String
representation of a matrix.String
by joining the elements with the
given glue.ByteProductQuantiser
by applying exact K-Means to
sub-vectors extracted from the given data.ByteProductQuantiser
by applying exact K-Means to
sub-vectors extracted from the given data.DoubleProductQuantiser
by applying exact K-Means to
sub-vectors extracted from the given data.DoubleProductQuantiser
by applying exact K-Means to
sub-vectors extracted from the given data.FloatProductQuantiser
by applying exact K-Means to
sub-vectors extracted from the given data.FloatProductQuantiser
by applying exact K-Means to
sub-vectors extracted from the given data.IntProductQuantiser
by applying exact K-Means to
sub-vectors extracted from the given data.IntProductQuantiser
by applying exact K-Means to
sub-vectors extracted from the given data.LongProductQuantiser
by applying exact K-Means to
sub-vectors extracted from the given data.LongProductQuantiser
by applying exact K-Means to
sub-vectors extracted from the given data.ShortProductQuantiser
by applying exact K-Means to
sub-vectors extracted from the given data.ShortProductQuantiser
by applying exact K-Means to
sub-vectors extracted from the given data.FontSimulator
)
and using the features extracted from those images, train a nearest
neighbour classifier.Normaliser
s that need to be pre-trained in order to
compute relevant statistics to perform the actual normalisation operation.ActiveShapeModel
using the given data and parameters.MultiResolutionActiveShapeModel
from the given
data.GeometricObject2d
and
return itPointList
and
return itTransformedDetection
with the given detected
object and transform.SingleImageTransferResidual2d
that
pre-transforms both sets of points by predetermined transforms.ClassTransformer
classes
when using the loading technique provided by ClassLoaderTransform.main(String[])
.ClassTransformer
s
when using the loading technique provided by ClassLoaderTransform.main(String[])
.Model
s that produce transform matrices (via the
MatrixTransformProvider
) that tests whether the condition number is
below a threshold.PointList
s positionTransforms.calculateIntensity(MBFImage)
.y = (A^T)x
.y = (A^T)Ax
.RetrievalAnalyser
that uses the trec_eval commandline tool to
perform the analysis.AnalysisResult
wrapping the output of the
trec_eval tool.TriangulatedPolygon
with a polygon.TriangulatedPolygon
with a shape.LanguageDetector
's responseAbstractTwitterPreprocessingToolOptions
and preprocess
tweetsTwitterStreamFactory
of twitter4j and oAuth usingAbstractTwitterPreprocessingToolOptions
from the
HadoopTwitterPreprocessingTool.ARGS_KEY
variable (once per in memory
mapper) and uses these to preprocess tweets.AbstractTwitterSearchDataset
which pushes
the Status
s into the stream.AbstractTwitterStreamDataset
which pushes
the Status
s into the stream.AbstractTwitterStreamDataset
which pushes
the Status
s into the stream.Status
objects to extract all the mentioned URLs.GroupedDataset
of UKBenchListDataset
s instances each of an
item in the UKBench experiment.NumAnnotationsChooser
to determine how
many annotations are produced by calls to UniformRandomAnnotator.annotate(Object)
.ListDataset
s.UniformRandomisedSampler
with the given percentage of
instances to select.UniformRandomisedSampler
with the given percentage of
instances to select, using with with-replacement or without-replacement
sampling.UniformRandomisedSampler
with the given number of
instances to select.UniformRandomisedSampler
with the given number of
instances to select, using with with-replacement or without-replacement
sampling.CollectionSampler
that performs uniform sampling.p1
and p2
where the return
type is of polyClass
.p1
and p2
where the return
type is of PolyDefault
.EstimatableModel
that uses a
VectorNaiveBayesCategorizer
to associate a univariate (a
Double
) with a category.KestrelThriftSpout
that is purposefully unreliable
and cuts other corners in an effort for improved speedscachedScale
of the StageTreeClassifier
).MBFImage
instance from the whole parent PApplet
MBFImage
instanceStreamJSONStatusList.ReadableWritableJSON
instanceSiteSpecificConsumer
instances loaded into
StatusConsumer.siteSpecific
.CmdLineException
if the
arguments are not valid.InOutToolOptions
and return
the corresponding file.InOutToolOptions
and return
the corresponding file.ValueAnimator.nextValue()
is
called, subject to some constraints.FacialKeypoint.FacialKeypointType
at the specified ordinalGroupedDataset
of VFSListDataset
s backed by directories of
items (either locally or remotely), or items stored in a hierarchical
structure within a compressed archive.ListDataset
backed by a directory of items (either locally or
remotely), or items stored in a compressed archive.InputStreamObjectReader
s be used as a
ObjectReader
with a FileObject
source type.VGet
, which supports video download from youtube, vimeo
and a few other video sites.ImageAnalyser
class for analysing
image.Video
that can capture live video streams
from a webcam or other video device.VideoCaptureException
with the specified detail message.VideoProcessor
that uses an ImageProcessor
for processing frames of a video.VideoDisplay
class that provides
GUI elements for starting, stopping, pausing and rewinding video.VideoShotDetector.setFPS(double)
)
when you know what that will be, otherwise your timecodes will
all be messed up.VideoFrame
with a subwindow definedVisualisationImageProvider.getVisualisationImage()
.VLAD
object and serialise it to a file.VLADIndexerData
which can be used to build efficient
Product-quantised PCA-VLAD indexes.VLADIndexerDataBuilder
with the given parametersWeightedBipolarSentiment
WeightedRectangle
with the given parameters.RotatedRectangle.getWidth()
which is the width of the regular
bounding box)ArrayBlockingDroppingQueue
of
capacity 1.Dataset
instance of the standard wine clustering experiment found
here:SWTTextDetector
.WordDFIDFTimeSeries
instancesLocalFeatureList
of FloatLocalFeatureAdaptor
by
wrapping the input list, and dynamically wrapping with the
FloatLocalFeatureAdaptor
s on demand.LocalFeatureList
of FloatLocalFeatureAdaptor
by
wrapping the input list, and dynamically wrapping with the
FloatLocalFeatureAdaptor
s on demand.LocalFeatureList
of FloatLocalFeatureAdaptor
by
wrapping the input list, and dynamically wrapping with the
FloatLocalFeatureAdaptor
s on demand.LocalFeatureList
of FloatLocalFeatureAdaptor
by
wrapping the input list, and dynamically wrapping with the
FloatLocalFeatureAdaptor
s on demand.Counter
written using the enum DataOutput
.VLADIndexerData
object to the given file.VLADIndexerData
object to the given stream.List
that is writeable.Map
that is writeable.Path
x
in a file filename
,
assuming that it is 1D complex array.x
in a file filename
,
assuming that it is 1D complex array.x
in a file filename
,
assuming that it is 2D complex array.x
in a file filename
,
assuming that it is 2D complex array.x
in a file filename
.x
in a file filename
,
assuming that it is 3D complex array.x
in a file filename
.x
in a file filename
,
assuming that it is 2D real array.x
in a file filename
,
assuming that it is 2D real array.x
in a file filename
,
assuming that it is 2D real array.x
in a file filename
,
assuming that it is 2D real array.x
in a file filename
,
assuming that it is 3D real array.MLCell
writen to a .mat data fileMLCell
writen to a .mat data filep1
and p2
where the return
type is of polyClass
.p1
and p2
where the return
type is of PolyDefault
.Video
interface.XYPlotVisualisation.LocatedObject
into 3D.YagoEntityCandidateFinderFactory.YagoEntityCandidateFinder
in various
ways.YagoEntityContextScorerFactory.YagoEntityContextScorer
YagoEntityExactMatcherFactory.YagoEntityExactMatcher
from provided resource folder or default.AchantaSaliency
and FelzenszwalbHuttenlocherSegmenter
.