Model-based matching using a hybrid genetic algorithm

This paper describes a hybrid genetic algorithm (HGA) for model-based matching of observed scenes that are noisy, where only a small fraction of the scene features are expected to correspond to the model features. The problem of finding the match is framed within a hypothesize-and-test paradigm and the HGA, with a representation size minimum description length evaluation function, is formulated as the method to search for the match. Unlike most genetic algorithms, the HGA introduced is based on an integer representation with a position based recombination operator. An assignment operator, also used as a reproduction operator, introduces domain-specific constraints and defines the hybrid nature of the algorithm. Results for models and scenes derived from images of occluded and cluttered environments are described. The results show the HGA to be an efficient search technique and the related matching technique to be robust in a variety of cases