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CAIP
2001
Springer
293views Image Analysis» more  CAIP 2001»
15 years 9 months ago
A Markov Random Field Image Segmentation Model Using Combined Color and Texture Features
In this paper, we propose a Markov random field (MRF) image segmentation model which aims at combining color and texture features. The theoretical framework relies on Bayesian est...
Zoltan Kato, Ting-Chuen Pong
AIPR
2005
IEEE
15 years 10 months ago
Face Recognition Using Multispectral Random Field Texture Models, Color Content, and Biometric Features
Most of the available research on face recognition has been performed using gray scale imagery. This paper presents a novel two-pass face recognition system that uses a Multispect...
Orlando J. Hernandez, Mitchell S. Kleiman
IVC
2007
95views more  IVC 2007»
15 years 4 months ago
Models from image triplets using epipolar gradient features
In an application where sparse matching of feature points is used towards fast scene reconstruction, the choice of the type of features to be matched has an important impact on th...
Étienne Vincent, Robert Laganière
MVA
2007
309views Computer Vision» more  MVA 2007»
15 years 4 months ago
Robust Facial Feature Extraction Using Embedded Hidden Markov Model for Face Recognition under Large Pose Variation
We propose an algorithm for extracting facial features robustly from images for face recognition under large pose variation. Rectangular facial features are retrieved via the by-p...
Ping-Han Lee, Yun-Wen Wang, Jison Hsu, Ming-Hsuan ...
ACL
2001
15 years 6 months ago
Improvement of a Whole Sentence Maximum Entropy Language Model Using Grammatical Features
In this paper, we propose adding long-term grammatical information in a Whole Sentence Maximun Entropy Language Model (WSME) in order to improve the performance of the model. The ...
Fredy A. Amaya, José-Miguel Benedí