FlowJ
Class FlowJLucas

java.lang.Object
  FlowJ.FlowJLucas

public class FlowJLucas
extends java.lang.Object

This is a class that implements the Lucas and Kanade optical flow algorithm. Implementation based on convolutions, with local field properties, gradient computation with different kernels including scalable Gaussian derivatives.

 References:
      Lucas and Kanade, Proc IJNA 1981
      Barron, Fleet, Beauchemin, 1994, IJCV 12, 1, 43-77
      Abramoff et al, 2002, IEEE TMI.
 

Copyright (c) 1999-2002, Michael Abramoff. All rights reserved.

Field Summary

boolean	debug
boolean	displayon
static java.lang.String[]	sderiv
static java.lang.String[]	sregul

Constructor Summary

FlowJLucas()

Method Summary

void	computeFull(FlowJFlow flow, boolean includeNormals, float sigmaw, float tau, int regularizationMethod) Compute full flow field from the first order gradients in dx, dy, dt for a weighted local neighborhood omega (weighted by G defined by sigmaw) around every image location.
void	filterAll(ij.ImageStack is, int center, float sigmat, float sigmas, int gradientType) Do filtering.
static int	firstFrame(ij.ImageStack is, float sigmat, int gradientType) Return the first frame for which you can compute the flow.
static int	getSupport(float sigmat, int gradientType) Return the number of frames needed for specific sigmat and gradient type.
static int	lastFrame(ij.ImageStack is, float sigmat, int gradientType) Return the last frame for which you can compute the flow.
java.lang.String	toString()

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

debug

public final boolean debug

See Also:

Constant Field Values

displayon

public final boolean displayon

See Also:

Constant Field Values

sderiv

public static java.lang.String[] sderiv

sregul

public static java.lang.String[] sregul

Constructor Detail

FlowJLucas

public FlowJLucas()

Method Detail

getSupport

public static int getSupport(float sigmat,
                             int gradientType)

Return the number of frames needed for specific sigmat and gradient type.

Parameters:

sigmat - temporal sigma of the kernel.

gradientType - the type of gradient, one of GAUSSIANDERIV, CENTRALDIFF, SOBEL2D, SOBEL3D, SUBTRACT

Returns:

number of frames needed.

firstFrame

public static int firstFrame(ij.ImageStack is,
                             float sigmat,
                             int gradientType)

Return the first frame for which you can compute the flow.

Parameters:

is - an ImageStack containing the images.

sigmat - temporal sigma of the kernel.

gradientType - the type of gradient, one of GAUSSIANDERIV, CENTRALDIFF, SOBEL2D, SOBEL3D, SUBTRACT

lastFrame

public static int lastFrame(ij.ImageStack is,
                            float sigmat,
                            int gradientType)

Return the last frame for which you can compute the flow.

Parameters:

is - an ImageStack containing the images.

sigmat - temporal sigma of the kernel.

gradientType - the type of gradient, one of GAUSSIANDERIV, CENTRALDIFF, SOBEL2D, SOBEL3D, SUBTRACT

filterAll

public void filterAll(ij.ImageStack is,
                      int center,
                      float sigmat,
                      float sigmas,
                      int gradientType)
               throws FlowJException

Do filtering.

Throws:

FlowJException

computeFull

public void computeFull(FlowJFlow flow,
                        boolean includeNormals,
                        float sigmaw,
                        float tau,
                        int regularizationMethod)

Compute full flow field from the first order gradients in dx, dy, dt for a weighted local neighborhood omega (weighted by G defined by sigmaw) around every image location. Find the best fit for that location using a LSE using the pseudoinverse. A SVD decreased speed and did not influence errors. All elements with eigenvalues of the pseudoinverse matrix m smaller than threshold tau are rejected. Only if the second eigenvalue of the pseudoinverse is larger than tau, normal flow instead of full flow is computed for that location.

Parameters:

flow - will contain the x,y flows.

includeNormals - a flag whether to include normals or not.

sigmaw - SD of the probability function associated with the Gaussian defining the size of the neighborhood

tau - the eigenvalue threshold

regularizationMethod - is one of REG_GAUSSIAN, REG_GAUSSIAN_1D, 0 defines how the local neighborhood is filtered.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object