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» Markov Random Field Modeling in Computer Vision
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ICML
2003
IEEE
14 years 10 months ago
Hidden Markov Support Vector Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines an...
Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofm...
ICPR
2000
IEEE
14 years 1 months ago
Realtime Online Adaptive Gesture Recognition
We introduce an online adaptive algorithm for learning gesture models. By learning gesture models in an online fashion, the gesture recognition process is made more robust, and th...
Andrew D. Wilson, Aaron F. Bobick
IJCAI
2007
13 years 10 months ago
Depth Estimation Using Monocular and Stereo Cues
Depth estimation in computer vision and robotics is most commonly done via stereo vision (stereopsis), in which images from two cameras are used to triangulate and estimate distan...
Ashutosh Saxena, Jamie Schulte, Andrew Y. Ng
ICML
2006
IEEE
14 years 10 months ago
Learning high-order MRF priors of color images
In this paper, we use large neighborhood Markov random fields to learn rich prior models of color images. Our approach extends the monochromatic Fields of Experts model (Roth &...
Alex J. Smola, Julian John McAuley, Matthias O. Fr...
ISMB
1997
13 years 10 months ago
Identifying Chimerism in Proteins Using Hidden Markov Models of Codon Usage
Protein chimerism is a phenomenon involving the combination of multiple ancestral sequences into a single, multi-domain protein through evolution. We propose a novel method for de...
Lawrence Hunter, Barry Zeeberg