Unsupervised Clustering in Hough Space for Identification of Partially Occluded Objects

An automated approach for template-free identification of partially occluded objects is presented. The contour of each relevant object in the analyzed scene is modeled with an approximating polygon whose edges are then projected into the Hough space. A structurally adaptive self-organizing map neural network generates clusters of collinear and/or parallel edges, which are used as the basis for identifying the partially occluded objects within each polygonal approximation. Results on a number of cases under different conditions are provided.

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