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Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion.
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Model-based hand tracking with texture, shading and self-occlusions
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Integrating bottom-up/top-down for object recognition by data driven Markov chain Monte Carlo
S.-C. Zhu, R. Zhang, and Z. Tu · 2000
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Robustly estimating changes in image appearance
M. J. Black, D. J. Fleet, and Y. Yacoob · 2000
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A Bayesian computer vision system for modeling human interactions
N. Oliver, B. Rosario, and A. Pentland · 2000
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Integrating bottom-up/top-down for object recognition by data driven Markov chain Monte Carlo
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L. Breiman · 2001
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A. B. Lee, D. Mumford, and J. Huang · 2001
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Pattern Theory: The Stochastic Analysis of Real-World Signals
D. Mumford and A. Desolneux · 2010
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S. Brooks, A. Gelman, G. Jones, and X.-L. Meng · 2011
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Real-time human pose recognition in parts from single depth images
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Enhanced computer vision with Microsoft Kinect sensor: A review
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Approximate Bayesian image interpretation using generative probabilistic graphics programs
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Computer vision for RGB-D sensors: Kinect and its applications
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