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Boosting a Biologically Inspired Local Descriptor for Geometry-free Face and Full Multi-view 3D Object Recognition

Author(s)
Yokono, Jerry Jun; Poggio, Tomaso
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Abstract
Object recognition systems relying on local descriptors are increasingly used because of their perceived robustness with respect to occlusions and to global geometrical deformations. Descriptors of this type -- based on a set of oriented Gaussian derivative filters -- are used in our recognition system. In this paper, we explore a multi-view 3D object recognition system that does not use explicit geometrical information. The basic idea is to find discriminant features to describe an object across different views. A boosting procedure is used to select features out of a large feature pool of local features collected from the positive training examples. We describe experiments on face images with excellent recognition rate.
Date issued
2005-07-07
URI
http://hdl.handle.net/1721.1/30557
Other identifiers
MIT-CSAIL-TR-2005-046
AIM-2005-023
CBCL-254
Series/Report no.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
Keywords
AI, 3D multiview, object recognition, SVM and boosting classifiers

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