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18 September 1998 Model evolution methodology for adaptive object recognition under dynamic perceptual conditions
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The paper presents a model evolution methodology for object Recognition under dynamic perceptual conditions. The methodology consists of a Model Application, a Model Evolution, and a Reinforcement Learning. The model application is an approach to the recognition of objects within a sequence of images, which have been acquired under dynamic perceptual conditions. In this approach an RBF (Radial Basis Function)- based classifier is applied to classify/segment objects within each image. The model evolution is concerned with the modification of models, which are created off-line or continue to be updated on-line. The purpose of the model evolution is that these models can adapt to next incoming images. The model evolution is achieved with the help of the reinforcement learning, which is activated to generate information for model evolution, when it is needed to modify models according to perceived disparities between the models and reality. The methodology has been achieved through the development of an adaptive vision system, which consists of three main subsystems: Model Application system, Reinforcement Learning system, and Model Evolution system. They have been developed and integrated in a close-loop so that object models can evolve to recognize objects under variable perceptual conditions.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sung Wook Baik and Peter W. Pachowicz "Model evolution methodology for adaptive object recognition under dynamic perceptual conditions", Proc. SPIE 3371, Automatic Target Recognition VIII, (18 September 1998);


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