Rapid and robust algorithms for detecting colour targets. 10Th Congress Of The International Colour Association, Aic Colour, 5, 959–962.. (2005).
Shape matching with belief propagation: Using dynamic quantization to accomodate occlusion and clutter. In Computer Vision and Pattern Recognition Workshop, 2004. CVPRW'04. Generative Model-Based Vision. (pp. 180–180). IEEE.. (2004).
Algorithms from statistical physics for generative models of images. Image And Vision Computing, 21, 29–36.. (2003).
A bayesian network framework for relational shape matching. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on (pp. 671–678). IEEE.. (2003).
The generic viewpoint assumption and planar bias. Ieee Transactions On Pattern Analysis And Machine Intelligence, 25, 775–778.. (2003).
The KGBR viewpoint-lighting ambiguity. Journal Of The Optical Society Of America (Josa) A, 20(1).. (2003).
A large deviation theory analysis of Bayesian tree search. Ima Volumes In Mathematics And Its Applications, 133, 1–18.. (2003).
Manhattan world: Orientation and outlier detection by bayesian inference. Neural Computation, 15, 1063–1088.. (2003).
A statistical approach to multi-scale edge detection. Image And Vision Computing, 21, 37–48.. (2003).
Statistical edge detection: Learning and evaluating edge cues. Pattern Analysis And Machine Intelligence, Ieee Transactions On, 25, 57–74.. (2003).
Bayesian A* tree search with expected O (N) node expansions: applications to road tracking. Neural Computation, 14, 1929–1958.. (2002).
Finding deformable shapes using loopy belief propagation. In Computer Vision—ECCV 2002 (pp. 453–468). Springer Berlin Heidelberg.. (2002).
Fundamental bounds on edge detection: learning and evaluating edge cues. Pattern Anal. Machine Intell.. (2002).
The g Factor: Relating Distributions on Features to Distributions on Images. In NIPS (pp. 1231–1238).. (2001).
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).
Order Parameters for Detecting Target Curves in Images: When does high level knowledge help?. International Journal Of Computer Vision, 41, 9–33.. (2001).
An A∗ perspective on deterministic optimization for deformable templates. Pattern Recognition, 33, 603–616.. (2000).
Efficient deformable template detection and localization without user initialization. Computer Vision And Image Understanding, 78, 303–319.. (2000).
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 generic viewpoint constraint resolves the generalized bas relief ambiguity. Proc. Of Conference On Information Scienes And Systems (Ciss 2000), 15–17.. (2000).
The Manhattan world assumption: Regularities in scene statistics which enable Bayesian inference. In NIPS (pp. 845–851).. (2000).
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).
Unified framework for performance analysis of Bayesian inference. In AeroSense 2000 (pp. 333–346). International Society for Optics and Photonics.. (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).
From Generic to Specific: An Information Theoretic Perspective on the Value of High-Level Information. Probabilistic Models Of The Brain, 135.. (1999).