Occluded object recognition using extended local features and hashing

We propose a new occluded object recognition method using extended local features and hashing. First we present some methods for extracting the extended local features such as corners, arcs, parallel-lines, and corner-arcs from the preprocessed images. Then we construct the knowledge-base using hashing, which can reduce the searching time significantly. In order to match the hypothesized objects, we find a geometric transform using clustering, which brings a model point to the corresponding image point. Our methods were tested on a hypercube-topology multiprocessor computer, the Intel iPSC/2.<
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