org.openimaj.image.pixel.statistics

Interface PixelProfileModel<I extends Image<?,I>>

Type Parameters:

I - Concrete type of Image

All Known Implementing Classes:

FStatisticalPixelProfileModel, MBFStatisticalPixelProfileModel

public interface PixelProfileModel<I extends Image<?,I>>

Interface for classes capable of building "models" of pixels along a line.

Author:

Jonathon Hare (jsh2@ecs.soton.ac.uk)

Method Summary

Modifier and Type	Method and Description
float	computeCost(I image, Line2d line) Compute the cost of a vector of samples extracted along a line in the given image to the internal model.
float	computeMovementDistance(I image, Line2d line, int numSamples, Point2d pt) Compute the distance between the centre of the given line and the given point, normalised as a function of the length of the sampling line.
Point2d	computeNewBest(I image, Line2d line, int numSamples) Extract numSamples samples from the line in the image and then compare this model at each overlapping position starting from the first sample at the beginning of the line.
void	updateModel(I image, Line2d line) Update the model with a new sample.

Method Detail

updateModel

void updateModel(I image,
                 Line2d line)

Update the model with a new sample.

Parameters:

image - the image to extract the sample from

line - the line across with to sample

computeNewBest

Point2d computeNewBest(I image,
                       Line2d line,
                       int numSamples)

Extract numSamples samples from the line in the image and then compare this model at each overlapping position starting from the first sample at the beginning of the line. numSamples must be bigger than the number of samples used to construct the model. In addition, callers are responsible for ensuring the sampling rate between the new samples and the model is equal. The point on the line corresponding to the smallest Mahalanobis distance is returned.

Parameters:

image - the image to sample

line - the line to sample along

numSamples - the number of samples to make

Returns:

the "best" position on the line

computeCost

float computeCost(I image,
                  Line2d line)

Compute the cost of a vector of samples extracted along a line in the given image to the internal model.

Parameters:

image - the image to sample

line - the line to sample along

Returns:

the computed cost

computeMovementDistance

float computeMovementDistance(I image,
                              Line2d line,
                              int numSamples,
                              Point2d pt)

Compute the distance between the centre of the given line and the given point, normalised as a function of the length of the sampling line.

Parameters:

image - the image to sample

line - the line to sample along

numSamples - the number of samples to make

pt - the point to compare

Returns:

the normalised distance (0 means same point; 1 means on end of line)