Pictorial Structures for Object Recognition
Pedro F. Felzenszwalb
Daniel P. Huttenlocher
IJCV 61(1), 2005
Abstract
In this paper we present a computationally efficient framework for part-based modeling and recognition of objects, motivated by the pictorial structure models introduced by Fischler and Elschlager. We address the problem of using pictorial structure models to find instances of an object in an image as well as the problem of learning an object model from training examples, presenting efficient algorithms in both cases. We demonstrate the techniques
by learning models that represent faces and human bodies and using the resulting
models to locate the corresponding objects in novel images.
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