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» Invariant Image Object Recognition Using Mixture Densities
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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
ICIP
2005
IEEE
16 years 1 months ago
Comparison of invariant descriptors for object recognition
This paper deals with the performance evaluation of three object invariant descriptors : Hu moments, Zernike moments and Fourier-Mellin descriptors. Experiments are conducted on a...
Anant Choksuriwong, Bruno Emile, Hélè...
CVPR
2000
IEEE
16 years 1 months ago
Towards Automatic Discovery of Object Categories
We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Ou...
Markus Weber, Max Welling, Pietro Perona
CVPR
2003
IEEE
16 years 1 months ago
Object Class Recognition by Unsupervised Scale-Invariant Learning
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constel...
Robert Fergus, Pietro Perona, Andrew Zisserman
BMVC
2010
14 years 9 months ago
Weakly Supervised Object Recognition and Localization with Invariant High Order Features
High order features have been proposed to incorporate geometrical information into the "bag of feature" representation. We propose algorithms to perform fast weakly supe...
Yimeng Zhang, Tsuhan Chen