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Published May 2004 | Accepted Version
Book Section - Chapter Open

Recognition by probabilistic hypothesis construction

Abstract

We present a probabilistic framework for recognizing objects in images of cluttered scenes. Hundreds of objects may be considered and searched in parallel. Each object is learned from a single training image and modeled by the visual appearance of a set of features, and their position with respect to a common reference frame. The recognition process computes identity and position of objects in the scene by finding the best interpretation of the scene in terms of learned objects. Features detected in an input image are either paired with database features, or marked as clutters. Each hypothesis is scored using a generative model of the image which is defined using the learned objects and a model for clutter. While the space of possible hypotheses is enormously large, one may find the best hypothesis efficiently-we explore some heuristics to do so. Our algorithm compares favorably with state-of-the-art recognition systems.

Additional Information

© 2004 Springer. The authors thank Prof. D.Lowe for kindly providing the code of his feature detector and for useful advice. They also acknowledge funding from the NSF Engineering Research Center on Neuromorphic Systems Engineering at Caltech.

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August 19, 2023
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