boofcv.alg.geo.trifocal

Class TrifocalAlgebraicPoint7

java.lang.Object

boofcv.alg.geo.trifocal.TrifocalLinearPoint7

boofcv.alg.geo.trifocal.TrifocalAlgebraicPoint7

public class TrifocalAlgebraicPoint7
extends TrifocalLinearPoint7

Initially computes the trifocal tensor using the linear method TrifocalLinearPoint7, but then iteratively refines the solution to minimize algebraic error by adjusting the two epipoles. The solution will enforce all the constraints and be geometrically valid. See page 395 in [1].

References:

• R. Hartley, and A. Zisserman, "Multiple View Geometry in Computer Vision", 2nd Ed, Cambridge 2003

Constructor Summary

Constructors

Constructor and Description
TrifocalAlgebraicPoint7(UnconstrainedLeastSquares optimizer, int maxIterations, double ftol, double gtol) Configures optimization algorithms

Method Summary

Modifier and Type	Method and Description
boolean	process(java.util.List<AssociatedTriple> observations, TrifocalTensor solution) Estimates the trifocal tensor given the set of observations

Methods inherited from class java.lang.Object

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

Constructor Detail

TrifocalAlgebraicPoint7

public TrifocalAlgebraicPoint7(UnconstrainedLeastSquares optimizer,
                               int maxIterations,
                               double ftol,
                               double gtol)

Configures optimization algorithms

Parameters:

optimizer - Which least squares minimizer should be used.

maxIterations - Maximum number of iterations it will optimize for

ftol - Convergence tolerance. See UnconstrainedLeastSquares for details.

gtol - Convergence tolerance. See UnconstrainedLeastSquares for details.

Method Detail

process

public boolean process(java.util.List<AssociatedTriple> observations,
                       TrifocalTensor solution)

Description copied from class: TrifocalLinearPoint7

Estimates the trifocal tensor given the set of observations

Overrides:

process in class TrifocalLinearPoint7

Parameters:

observations - Set of observations

solution - Output: Where the solution is written to

Returns:

true if successful and false if it fails