Genetic contour matching

Object recognition can be formulated as an optimization problem. The objective function measures for instance the evidential support for any particular projection of the parameterized object contour model onto the input image. A genetic algorithm can be used to find a set of parameters which provide an optimal interpretation of the image in terms of the model. Preliminary test results demonstrate the feasibility of the proposed approach.

[1]  Robert R. Goldberg,et al.  Pose determination of parameterized object models from a monocular image , 1993, Image Vis. Comput..

[2]  Azriel Rosenfeld,et al.  Digital Picture Processing, Volume 1 , 1982 .

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  Azriel Rosenfeld,et al.  Compact Object Recognition Using Energy-Function-Based Optimization , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Mandyam D. Srinath,et al.  Partial Shape Classification Using Contour Matching in Distance Transformation , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Azriel Rosenfeld,et al.  Digital Picture Processing , 1976 .

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[9]  Badrinath Roysam,et al.  A genetic algorithm for intelligent imaging from quantum-limited data , 1992, Signal Process..

[10]  Gunilla Borgefors,et al.  Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Ben J. H. Verwer,et al.  Local distances for distance transformations in two and three dimensions , 1991, Pattern Recognit. Lett..

[12]  Mats Bengtsson.  Stochastic optimization algorithms - an application to pattern matching , 1990, Pattern Recognit. Lett..

[13]  Aaron Heller,et al.  The evolution and testing of a model-based object recognition system , 1990, [1990] Proceedings Third International Conference on Computer Vision.