Recognition of partially occluded objects using neural network based indexing
In this paper, a new neural network based indexing scheme has been proposed for recognition of planar shapes. Local contour segment-based-invariants have been used for indexing. Object contours have been obtained using a new algorithm which combines advantages of region growing and edge detection. Neighbourhood constraints have been applied on the results of indexing for combining hypotheses generated through the indexing scheme. Composite hypotheses have been verified using a distance transform based algorithm. Experimental results, on real images of varying complexity of a reasonably large database of objects have established the robustness of the method. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
neural networks object recognition invariant indexing hypothesize-and-test contour segments