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Issue No.02 - February (1998 vol.20)
pp: 186-192
ABSTRACT
<p><b>Abstract</b>—Verification is the final decision stage in many object recognition processes. It is carried out by evaluating a score for every hypothesis and choosing the hypotheses associated with the highest score. This paper suggests a grouping-based verification paradigm, relying on the observation that a group of data features belonging to a hypothesized object instance should be a "good group." Therefore, it should support perceptual grouping information available from the image by grouping relations. The proposed score, which is the joint likelihood of these grouping cues, quantifies this observation in a probabilistic framework. Experiments with synthetic and real images show that the proposed method performs better in difficult cases.</p>
INDEX TERMS
Hypothesis verification, object recognition, perceptual grouping, maximum likelihood, graph clustering, Kullback Leibler distance.
CITATION
Arnon Amir, Michael Lindenbaum, "Grouping-Based Nonadditive Verification", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.20, no. 2, pp. 186-192, February 1998, doi:10.1109/34.659936
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