Use neural networks to determine matching order for recognizing overlapping objects

In traditional model-based object recognition systems, a model of each object in the model database is matched in a random sequence against the scene image. The matching procedure must be repeated for every model in the database until a correct match is found. The major problem with such an approach is that as the number of models is increased the computational time required to find the correct match becomes very high. In this paper, we present an artificial neural network (ANN) approach to determine the matching order of models in the database. The match between a given scene image and a model is based on the rank of similarity of the model rather than its serial storage order in the database. Both isolated and overlapping objects that comprise piecewise linear and circular segments are considered for the recognition. Experimental results have shown that the proposed ANN approach succeeds in recognizing isolated objects, and achieves significant gain, in number of matches, over traditional model-based object recognition systems for identifying overlapping objects.

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