Uses of Interface
org.openimaj.math.model.EstimatableModel

Packages that use EstimatableModel

Package	Description
org.openimaj.image.model
org.openimaj.image.model.patch
org.openimaj.image.model.pixel
org.openimaj.math.geometry.transforms
org.openimaj.math.model
org.openimaj.math.model.fit
org.openimaj.ml.annotation.model
org.openimaj.ml.linear.learner.perceptron
org.openimaj.ml.regression

Uses of EstimatableModel in org.openimaj.image.model

Subinterfaces of EstimatableModel in org.openimaj.image.model

Modifier and Type	Interface and Description
interface	ImageClassificationModel<T extends Image<?,T>> An ImageClassificationModel is a EstimatableModel constructed between an generic image and a probability map in the form of an FImage.

Uses of EstimatableModel in org.openimaj.image.model.patch

Classes in org.openimaj.image.model.patch that implement EstimatableModel

Modifier and Type	Class and Description
class	HistogramPatchModel A 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.
class	MBFPatchClassificationModel A base implementation of a PatchClassificationModel for MBFImages.
class	PatchClassificationModel<Q,T extends Image<Q,T>> An 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.

Uses of EstimatableModel in org.openimaj.image.model.pixel

Classes in org.openimaj.image.model.pixel that implement EstimatableModel

Modifier and Type	Class and Description
class	HistogramPixelModel An MBFPixelClassificationModel that classifies an individual pixel by comparing it to a joint (colour) histogram.
class	MBFPixelClassificationModel An abstract PixelClassificationModel for MBFImages.
class	OrthotopePixelModel Model of an orthotope/hyperrectangle/box in space.
class	PixelClassificationModel<Q,T extends Image<Q,T>> Simple model for classifying pixels.
class	SingleGaussianPixelModel An MBFPixelClassificationModel that classifies an individual pixel by comparing it to a CachingMultivariateGaussian.

Uses of EstimatableModel in org.openimaj.math.geometry.transforms

Classes in org.openimaj.math.geometry.transforms that implement EstimatableModel

Modifier and Type	Class and Description
class	AffineTransformModel Concrete implementation of a model of an Affine transform.
class	AffineTransformModel3d Concrete implementation of a model of an Affine transform.
class	FundamentalModel Implementation of a Fundamental matrix model that estimates the epipolar geometry.
class	HomographyModel Implementation of a Homogeneous Homography model - a transform that models the relationship between planes under projective constraints (8 D.o.F)
class	NullModel<T> A NullModel models a one-to-one mapping of data.
class	RigidTransformModel3d Concrete implementation of a model of a 3D rigid transform with only rotation and translation allowed.

Uses of EstimatableModel in org.openimaj.math.model

Classes in org.openimaj.math.model that implement EstimatableModel

Modifier and Type	Class and Description
class	GaussianVectorNaiveBayesModel<T> An implementation of a EstimatableModel that uses a VectorNaiveBayesCategorizer to associate vectors (actually double[]) with a category based on the naive bayes model.
class	LeastSquaresLinearModel Model of mapping between pairs of integers learned from a least-squares regression.
class	UnivariateGaussianNaiveBayesModel<T> An implementation of a EstimatableModel that uses a VectorNaiveBayesCategorizer to associate a univariate (a Double) with a category.

Methods in org.openimaj.math.model that return EstimatableModel

Modifier and Type	Method and Description
EstimatableModel<I,D>	EstimatableModel.clone() Clone the model

Uses of EstimatableModel in org.openimaj.math.model.fit

Classes in org.openimaj.math.model.fit with type parameters of type EstimatableModel

Modifier and Type	Class and Description
class	LMedS<I,D,M extends EstimatableModel<I,D>> Least Median of Squares robust model fitting
class	RANSAC<I,D,M extends EstimatableModel<I,D>> The RANSAC Algorithm (RANdom SAmple Consensus)
class	SimpleModelFitting<I,D,M extends EstimatableModel<I,D>> Example robust fitting, that simply wraps the models estimate method.

Fields in org.openimaj.math.model.fit declared as EstimatableModel

Modifier and Type	Field and Description
protected M	LMedS.bestModel
protected M	RANSAC.model
protected M	LMedS.model

Methods in org.openimaj.math.model.fit with parameters of type EstimatableModel

Modifier and Type	Method and Description
boolean	RANSAC.StoppingCondition.init(List<?> data, EstimatableModel<?,?> model) Initialise the stopping condition if necessary.
boolean	RANSAC.NumberInliersStoppingCondition.init(List<?> data, EstimatableModel<?,?> model)
boolean	RANSAC.PercentageInliersStoppingCondition.init(List<?> data, EstimatableModel<?,?> model)
boolean	RANSAC.ProbabilisticMinInliersStoppingCondition.init(List<?> data, EstimatableModel<?,?> model)
boolean	RANSAC.BestFitStoppingCondition.init(List<?> data, EstimatableModel<?,?> model)

Uses of EstimatableModel in org.openimaj.ml.annotation.model

Constructors in org.openimaj.ml.annotation.model with parameters of type EstimatableModel

Constructor and Description
ModelAnnotator(FeatureExtractor<FEATURE,OBJECT> extractor, EstimatableModel<FEATURE,ANNOTATION> model, Set<ANNOTATION> annotations) Construct with the given parameters.

Uses of EstimatableModel in org.openimaj.ml.linear.learner.perceptron

Classes in org.openimaj.ml.linear.learner.perceptron that implement EstimatableModel

Modifier and Type	Class and Description
class	SimplePerceptron

Uses of EstimatableModel in org.openimaj.ml.regression

Classes in org.openimaj.ml.regression that implement EstimatableModel

Modifier and Type	Class and Description
class	LinearRegression Given a set of independant variables a linear regressions finds the optimal vector B such that: (Y - Xb)^2 = 0 (Y - Xb)^{T}(Y-Xb) = 0 calculated by assuming a convex shape of (Y - Xb) with varying values of b (reasonable as the function is linear) and then calculating the point at which the first derivative of this function is 0.