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ECCV
2004
Springer

Recognition by Probabilistic Hypothesis Construction

15 years 2 months ago
Recognition by Probabilistic Hypothesis Construction
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.
Pierre Moreels, Michael Maire, Pietro Perona
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2004
Where ECCV
Authors Pierre Moreels, Michael Maire, Pietro Perona
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