A new constrained parameter estimator for computer vision applications
A method of constrained parameter estimation is proposed for a class of computer vision problems. In a typical application, the parameters will describe a relationship between image feature locations, expressed as an equation linking the parameters and the image data, and will satisfy an ancillary constraint not involving the image data. A salient feature of the method is that it handles the ancillary constraint in an integrated fashion, not by means of a correction process operating upon results of unconstrained minimisation. The method is evaluated through experiments in fundamental matrix computation. Results are given for both synthetic and real images. It is demonstrated that the method produces results commensurate with, or superior to, previous approaches, with the advantage of being faster than comparable techniques.
ancillary constraint fundamental matrix gaussian errors maximum likelihood constrained minimisation epipolar equation singularity constraint