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Publication . Article . 2005

Pictorial Structures for Object Recognition

Felzenszwalb, Pedro F.; Huttenlocher, Daniel P.;
Published: 01 Jan 2005
Publisher: Springer Science and Business Media LLC
In this paper we present a computationally efficient framework for part-based modeling and recognition of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to represent an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. 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.
Subjects by Vocabulary

Microsoft Academic Graph classification: Object model Cognitive neuroscience of visual object recognition 3D single-object recognition Pattern recognition (psychology) Object (computer science) Articulated body pose estimation Computer vision Artificial intelligence business.industry business Visual appearance Method Computer science


Artificial Intelligence, Computer Vision and Pattern Recognition, Software

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