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Algorithms from statistical physics for generative models of images. Image And Vision Computing, 21, 29–36.. (2003).
An A∗ perspective on deterministic optimization for deformable templates. Pattern Recognition, 33, 603–616.. (2000).
Bayesian A* tree search with expected O (N) convergence rates for road tracking. In Energy Minimization Methods in Computer Vision and Pattern Recognition (pp. 189–204). Springer Berlin Heidelberg.. (1999).
Bayesian A* tree search with expected O (N) node expansions: applications to road tracking. Neural Computation, 14, 1929–1958.. (2002).
A bayesian network framework for relational shape matching. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on (pp. 671–678). IEEE.. (2003).
Convergence rates of algorithms for visual search: detecting visual contours. In NIPS (pp. 641–647).. (1998).
Efficient deformable template detection and localization without user initialization. Computer Vision And Image Understanding, 78, 303–319.. (2000).
Efficient optimization of a deformable template using dynamic programming. In 2013 IEEE Conference on Computer Vision and Pattern Recognition (pp. 747–747). IEEE Computer Society.. (1998).
From Generic to Specific: An Information Theoretic Perspective on the Value of High-Level Information. Probabilistic Models Of The Brain, 135.. (1999).
Fundamental bounds on edge detection: An information theoretic evaluation of different edge cues. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on. (Vol. 1). IEEE.. (1999).
Fundamental bounds on edge detection: learning and evaluating edge cues. Pattern Anal. Machine Intell.. (2002).
Fundamental limits of Bayesian inference: order parameters and phase transitions for road tracking. Pattern Analysis And Machine Intelligence, Ieee Transactions On, 22, 160–173.. (2000).
The g Factor: Relating Distributions on Features to Distributions on Images. In NIPS (pp. 1231–1238).. (2001).
The generic viewpoint assumption and planar bias. Ieee Transactions On Pattern Analysis And Machine Intelligence, 25, 775–778.. (2003).
The generic viewpoint constraint resolves the generalized bas relief ambiguity. Proc. Of Conference On Information Scienes And Systems (Ciss 2000), 15–17.. (2000).
High-Level and Generic Models for Visual Search: When does high level knowledge help?. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on. (Vol. 2). IEEE.. (1999).
The KGBR viewpoint-lighting ambiguity. Journal Of The Optical Society Of America (Josa) A, 20(1). (Original work published 2003). (2003).
The KGBR viewpoint-lighting ambiguity and its resolution by generic constraints. Computer Vision, 2001. Iccv 2001. Proceedings. Eighth Ieee International Conference On, 2, 376–382.. (2001).
A large deviation theory analysis of Bayesian tree search. Ima Volumes In Mathematics And Its Applications, 133, 1–18.. (2003).
The Manhattan world assumption: Regularities in scene statistics which enable Bayesian inference. In NIPS (pp. 845–851).. (2000).
Manhattan world: Compass direction from a single image by bayesian inference. In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on (Vol. 2, pp. 941–947). IEEE.. (1999).
Manhattan world: Orientation and outlier detection by bayesian inference. Neural Computation, 15, 1063–1088.. (2003).
Order Parameters for Detecting Target Curves in Images: When does high level knowledge help?. International Journal Of Computer Vision, 41, 9–33.. (2001).
Order Parameters for Minimax Entropy Distributions: When does high level knowledge help?. In Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on (Vol. 1, pp. 558–565). IEEE.. (2000).