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Occluded object recognition using extended local features and hashing

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2 Author(s)
Joong-hwan Baek ; Dept. of Telecom. & Info. Eng., Hankuk Aviation Univ., Koyang-city, South Korea ; Teague, K.A.

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

Published in:

Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on  (Volume:3 )

Date of Conference:

2-5 Oct 1994

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