Alan Yuille

Coughlan, J. ., & Yuille, A. L. (1998). A phase space approach to minimax entropy learning and the minutemax approximations. NIPS, 761–767.

Yuille, A. L., Coughlan, J. ., Zhu, S. C., & Wu, Y. . (2000). Order Parameters for Minimax Entropy Distributions: When does high level knowledge help?. Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on, 1, 558–565. IEEE.

Yuille, A. L., Coughlan, J. ., & Konishi, S. . (2000). The generic viewpoint constraint resolves the generalized bas relief ambiguity. Proc. Of Conference on Information Scienes and Systems (CISS 2000), 15–17.

Yuille, A. L., & Coughlan, J. . (1998). Convergence rates of algorithms for visual search: detecting visual contours. NIPS, 641–647.

Coughlan, J. ., & Yuille, A. L. (2000). The Manhattan world assumption: Regularities in scene statistics which enable Bayesian inference. NIPS, 845–851.

Yuille, A. L., & Coughlan, J. . (2000). An A∗ perspective on deterministic optimization for deformable templates. Pattern Recognition, 33, 603–616.

Yuille, A. L., Coughlan, J. ., Wu, Y. ., & Zhu, S. C. (2001). Order Parameters for Detecting Target Curves in Images: When does high level knowledge help?. International Journal of Computer Vision, 41, 9–33.

Yuille, A. L., & Coughlan, J. . (1999). High-Level and Generic Models for Visual Search: When does high level knowledge help?. Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference On., 2. IEEE.

Coughlan, J. ., & Yuille, A. L. (2002). Bayesian A* tree search with expected O (N) node expansions: applications to road tracking. Neural Computation, 14, 1929–1958.

Yuille, A. L., & Coughlan, J. . (1999). 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. (Original work published 1999)