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Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification of Shadow Shapes in Sonar Imagery
February 2000 (vol. 22 no. 2)
pp. 129-141

Abstract—We present an original statistical classification method using a deformable template model to separate natural objects from man-made objects in an image provided by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is captured by a prototype template, along with a set of admissible linear transformations, to take into account the shape variability. Then, the classification problem is defined as a two-step process. First, the detection problem of a region of interest in the input image is stated as the minimization of a cost function. Second, the value of this function at convergence allows one to determine whether the desired object is present or not in the sonar image. The energy minimization problem is tackled using relaxation techniques. In this context, we compare the results obtained with a deterministic relaxation technique (a gradient-based algorithm) and two stochastic relaxation methods: Simulated Annealing (SA) and a hybrid Genetic Algorithm (GA). This latter method has been successfully tested on real and synthetic sonar images, yielding very promising results.

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Index Terms:
Deformable template, objective function, simulated annealing, gradient-based algorithm, genetic optimization, shape recognition, sonar imagery.
Citation:
Max Mignotte, Christophe Collet, Patrick Pérez, Patrick Bouthemy, "Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification of Shadow Shapes in Sonar Imagery," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 2, pp. 129-141, Feb. 2000, doi:10.1109/34.825752
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