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» Markov Random Field Models in Computer Vision
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CVPR
1998
IEEE
16 years 3 months ago
Nonlinear PHMMs for the Interpretation of Parameterized Gesture
In previous work [14], we modify the hidden Markov model (HMM) framework to incorporate a global parametric variation in the output probabilities of the states of the HMM. Develop...
Andrew D. Wilson, Aaron F. Bobick
ISCAS
2007
IEEE
106views Hardware» more  ISCAS 2007»
15 years 8 months ago
Ensemble Dependent Matrix Methodology for Probabilistic-Based Fault-tolerant Nanoscale Circuit Design
—Two probabilistic-based models, namely the Ensemble-Dependent Matrix model [1][3] and the Markov Random Field model [2], have been proposed to deal with faults in nanoscale syst...
Huifei Rao, Jie Chen, Changhong Yu, Woon Tiong Ang...
ICPR
2002
IEEE
16 years 2 months ago
Tracking People
This paper describes a real-time computer vision system for tracking people in monocular video sequences. The system tracks people as they move through the camera's field of ...
Ng Kim Piau, Surendra Ranganath
ECCV
2002
Springer
16 years 3 months ago
Statistical Modeling of Texture Sketch
Recent results on sparse coding and independent component analysis suggest that human vision first represents a visual image by a linear superposition of a relatively small number ...
Ying Nian Wu, Song Chun Zhu, Cheng-en Guo
ICCV
2003
IEEE
16 years 3 months ago
Photo-Consistent 3D Fire by Flame-Sheet Decomposition
This paper considers the problem of reconstructing visually realistic 3D models of fire from a very small set of simultaneous views (even two). By modeling fire as a semi-transpar...
Samuel W. Hasinoff, Kiriakos N. Kutulakos