Object Recognition With Deformable Models

The problem of detecting and localizing objects in images has important applications in a variety of areas, including robotics, image retrieval and medical image analysis. Deformable models represent objects as deformed versions of an ideal template. While this approach provides an elegant framework for object recognition, it also leads to difficult computational problems. The first part of this University of Washington program describes efficient algorithms that have been developed for finding objects in images using different types of deformable models. In the second part, Pedro Felzenszwalb of the University of Chicago considers the specific problem of detecting objects from generic categories such as people and cars in realistic scenes.

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