HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing general curved objects efficiently
Geometric invariance in computer vision
Computer
Parts of Visual Form: Computational Aspects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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This paper is focused on the object recognition problem in computer vision under partial occlusion. The approach followed to carry out this goal is the alignment method described exhaustively in the literature. In this approach the recognition process is divided in two stages: in a first stage, the transformation in space between the viewed object and the model object is determined. In a second stage the model that best matches the viewed object is found. Given four points in the image, it is necessary to find the four corresponding points in the model. This problem involving combinatorial search is resolved by means of a genetic algorithm. The occlusion problem has been dealt with special attention, so a new method has been proposed consisting of three processes: identification, grouping and verification. The recognition algorithm proposed here has been tested in several examples obtaining good results.