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Visual search: Fundamental bounds, order parameters, and phase transitions. Proc Ieee Workshop On Statistical And Computational Theories Of Vision. Cvpr 1999. Fort Collins, Co. June 1999.. (1999).
Unified framework for performance analysis of Bayesian inference. In AeroSense 2000 (pp. 333–346). International Society for Optics and Photonics.. (2000).
Twenty Questions, Focus of Attention, and A*: A theoretical comparison of optimization strategies. In Energy Minimization Methods in Computer Vision and Pattern Recognition (pp. 195–212). Springer Berlin Heidelberg.. (1997).
Statistical edge detection: Learning and evaluating edge cues. Pattern Analysis And Machine Intelligence, Ieee Transactions On, 25, 57–74.. (2003).
A statistical approach to multi-scale edge detection. Image And Vision Computing, 21, 37–48.. (2003).
A phase space approach to minimax entropy learning and the minutemax approximations. In NIPS (pp. 761–767).. (1998).
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).
Order Parameters for Detecting Target Curves in Images: When does high level knowledge help?. International Journal Of Computer Vision, 41, 9–33.. (2001).
Manhattan world: Orientation and outlier detection by bayesian inference. Neural Computation, 15, 1063–1088.. (2003).
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).
The Manhattan world assumption: Regularities in scene statistics which enable Bayesian inference. In NIPS (pp. 845–851).. (2000).
A large deviation theory analysis of Bayesian tree search. Ima Volumes In Mathematics And Its Applications, 133, 1–18.. (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).
The KGBR viewpoint-lighting ambiguity. Journal Of The Optical Society Of America (Josa) A, 20(1).. (2003).
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 generic viewpoint constraint resolves the generalized bas relief ambiguity. Proc. Of Conference On Information Scienes And Systems (Ciss 2000), 15–17.. (2000).
The generic viewpoint assumption and planar bias. Ieee Transactions On Pattern Analysis And Machine Intelligence, 25, 775–778.. (2003).
The g Factor: Relating Distributions on Features to Distributions on Images. In NIPS (pp. 1231–1238).. (2001).
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).
Fundamental bounds on edge detection: learning and evaluating edge cues. Pattern Anal. Machine Intell.. (2002).
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).
From Generic to Specific: An Information Theoretic Perspective on the Value of High-Level Information. Probabilistic Models Of The Brain, 135.. (1999).
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).
Efficient deformable template detection and localization without user initialization. Computer Vision And Image Understanding, 78, 303–319.. (2000).
Convergence rates of algorithms for visual search: detecting visual contours. In NIPS (pp. 641–647).. (1998).