Deformable templates, robust statistics, and Hough transforms
Alan Yuille, Carsten Peterson, Ko Honda · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
Deformable templates provide a method for feature extraction and object recognition. There are two basic ingredients to these templates: (i) a priori knowledge about the features to be extracted, which is embedded in the parameterized form of the template and by prior probabilities on the parameter values, and (ii) a matching criterion between the template and the image. In addition an algorithm must be specified to determine the optimal fit of the template. This paper defines deformable templates within a theoretical framework based on ideas from statistical physics. This framework enables one to incorporate standard techniques from robust statistics in a straightforward manner. These techniques are desirable since they allow the matching criteria to be robust and independent of outliers in the data. Matching criteria will then typically correspond to mixtures of distributions. It is then proved that by using the robust matching criteria and taking the limit of the statistical system as the temperature goes to zero, the standard Hough/Radon transform can be redriven. This can be used as a starting point for a deterministic annealing algorithm for matching the template. These ideas are illustrated with an example on detecting particle tracks in high energy physics experiments.