A fast sequential method for polygonal approximation of digitized curves
Computer Vision, Graphics, and Image Processing
HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relaxation labelling algorithms-a review
Image and Vision Computing
Localizing Overlapping Parts by Searching the Interpretation Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partial Shape Recognition Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
A simple approach for the estimation of circular arc center and its radius
Computer Vision, Graphics, and Image Processing
A neural network approach to robust shape classification
Pattern Recognition
Recognition of occluded objects with heuristic search
Pattern Recognition
Partial Shape Classification Using Contour Matching in Distance Transformation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new approach to polygonal approximation
Pattern Recognition Letters
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Non-linear alignment of neural net outputs for partial shape classification
Pattern Recognition
Real-time part position sensing
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
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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.