Abstract
Industrial vision systems should be capable of recognising noisy objects, partially occluded objects and randomly located and/or oriented objects. This paper considers the problem of recognition of partially occluded planar shapes using contour segment-based features. None of the techniques suggested in the literature for solving the above problem guarantee reliable results for problem instances which require memory in excess of what is available. In this paper, a heuristic search-based recognition algorithm is presented, which guarantees reliable recognition results even when memory is limited. This algorithm identifies an object, the maximum portion of whose contour is visible in a conglomerate of objects. For increasing efficiency of the method, a two-stage recognition scheme has been designed. In the first phase, a relevant subset of the known model shapes is chosen and in the second stage, matching between the unknown shape and elements of the relevant subset is attempted using the above approach. The technique is general in the sense that it can be used with any kind of contour features. To evaluate the efficiency of the method, experimentation was carried out using polygonal approximations of the object contours. Results are cited for establishing the effectiveness of the approach.
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