The Taub Faculty of Computer Science Events and Talks

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