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Pixel Club Seminar: Using A-Contrario Framework for Object Recognition
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Artiom Myaskouvskey (CS, Technion)
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Monday, 7.12.2009, 14:30
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Taub 601
We describe a new part based detection algorithm. The proposed algorithm uses an empirical model, based on data extracted from the test image to bound the probability that a model candidate arises from a noncategory-related image. The decision is adaptive and does not rely on parameters optimized for possibly unrelated non-category images. Finally, the decision process provides an bound on the number of false alarms in the image. We explain the a-contrario method, derive the empirical model, and specify a principled detection procedure for simple models and for more complex ones. Experiments show the validity of the predictions and their utility for setting detection parameters. *Msc research under the supervision of Prof. Micha Lindenbaum
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