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CVPR
2004
IEEE
16 years 1 months ago
Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting
We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter...
Fu Jie Huang, Léon Bottou, Yann LeCun
CVPR
1997
IEEE
16 years 1 months ago
Pictorial Recognition Using Affine-Invariant Spectral Signatures
This paper describes an efficient approach to pose invariant object recognition employing pictorial recognition of image patches. A complete affine invariance is achieved by a rep...
Jezekiel Ben-Arie, Zhiqian Wang
ECCV
2006
Springer
16 years 1 months ago
Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition
Abstract. In this paper we investigate a new method of learning partbased models for visual object recognition, from training data that only provides information about class member...
David J. Crandall, Daniel P. Huttenlocher
WACV
2005
IEEE
15 years 5 months ago
Incorporating Background Invariance into Feature-Based Object Recognition
Current feature-based object recognition methods use information derived from local image patches. For robustness, features are engineered for invariance to various transformation...
Andrew N. Stein, Martial Hebert
CVPR
2007
IEEE
16 years 1 months ago
Composite Models of Objects and Scenes for Category Recognition
This paper presents a method of learning and recognizing generic object categories using part-based spatial models. The models are multiscale, with a scene component that specifie...
David J. Crandall, Daniel P. Huttenlocher