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HEIV based estimation
Author: Bogdan Georgescu
Robust Image Understanding Laboratory, Rutgers University
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Implements the multivariate HEIV estimator based on:
B. Matei, P. Meer, "Estimation of nonlinear errors-in-variables models
for computer vision applications." IEEE Trans. Pattern Anal. Machine
Intell., 28, 1537-1552, 2006.  
Examples for using the base class for linear, ellipse, fundamental matrix and trifocal tensor
estimation are included in the program.

Using the binary:

heiv [-METHOD] [DATAFILE]

METHOD:
-l       linear HEIV
-e       ellipse HEIV
-fc      fundamental matrix constrained HEIV
-fu      fundamental matrix unconstrained HEIV
-t       trifocal tensor HEIV

DATAFILE format (see also the examples):
BgMatrix
nRows nColumns
ASCII
input data points, each row is a measurement

the estimate and the corrected measurements are written in out.txt

Using the sources:

the project files for MS Visual C++ are included. To compile the code you need to
have clapack (www.netlib.org/clapack/index.html) installed.
To use the HEIV algorithm for a different problem, derive a class from BgHeiv class
and write the methods for ZiXi and JiXi. ZiXi computes the carrier matrix elements Zi
corresponding to the current measurement Xi. JiXi computes the Jacobian Ji of the carrier
matrix corresponding to the current measurement Xi. See the examples provided in the code.

Bogdan Georgescu
georgesc@caip.rutgers.edu

Robust Image Understanding Laboratory
www.caip.rutgers.edu/riul