Error Of Fit Measures For Recovering Parametric Solids
Ari Gross, Terrance E. Boult · 2005
Parametric models of objects are becoming increasingly more impor- tant in computer vision. In the past few years, a number of researchers have investigated the recovery of a class of parametric models by the minimization of an error of fit measure. The measures used have typ- ically been chosen in an ad hoc fashion. This paper looks at how these measures affect the performance of a recovery system. This research can be divided into two parts. The first studies the biases of the po- tential error-of-fit measures with respect to the parameters recovered and examines the cross-sectional shape of their respective error of fit surfaces. This study is done in simulation by holding all but one pa- rameter constant. The second part of the research compares two of the better error of fit measures by using them in a recovery system. Both the number of iterations and the quality of the reconstruction are considered.