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. (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)

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.