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» Discriminative Training of Gaussian Mixtures for Image Objec...
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77
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AVBPA
2001
Springer
145views Biometrics» more  AVBPA 2001»
15 years 2 months ago
Using Mixture Covariance Matrices to Improve Face and Facial Expression Recognitions
In several pattern recognition problems, particularly in image recognition ones, there are often a large number of features available, but the number of training samples for each p...
Carlos E. Thomaz, Duncan Fyfe Gillies, Raul Queiro...
80
Voted
CVPR
2010
IEEE
15 years 3 months ago
Many-to-one Contour Matching for Describing and Discriminating Object Shape
We present an object recognition system that locates an object, identifies its parts, and segments out its contours. A key distinction of our approach is that we use long, salien...
Praveen Srinivasan, Qihui Zhu, Jianbo Shi
100
Voted
GW
2005
Springer
129views Biometrics» more  GW 2005»
15 years 3 months ago
Visual Sign Language Recognition Based on HMMs and Auto-regressive HMMs
Abstract. A sign language recognition system based on Hidden Markov Models(HMMs) and Auto-regressive Hidden Markov Models(ARHMMs) has been proposed in this paper. ARHMMs fully cons...
Xiaolin Yang, Feng Jiang, Han Liu, Hongxun Yao, We...
FGR
2000
IEEE
181views Biometrics» more  FGR 2000»
15 years 1 months ago
Face Detection Using Mixtures of Linear Subspaces
We present two methods using mixtures of linear subspaces for face detection in gray level images. One method uses a mixture of factor analyzers to concurrently perform clustering...
Ming-Hsuan Yang, Narendra Ahuja, David J. Kriegman
81
Voted
ICASSP
2011
IEEE
14 years 1 months ago
Bayesian sensing hidden Markov models for speech recognition
We introduce Bayesian sensing hidden Markov models (BS-HMMs) to represent speech data based on a set of state-dependent basis vectors. By incorporating the prior density of sensin...
George Saon, Jen-Tzung Chien