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.
James Coughlan
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)
Coughlan, J. ., & Shen, H. . (2007). Dynamic quantization for belief propagation in sparse spaces. Computer Vision and Image Understanding, 106, 47–58.
Yuille, A. L., & Coughlan, J. . (1998). Convergence rates of algorithms for visual search: detecting visual contours. NIPS, 641–647.
Shen, H. ., & Coughlan, J. . (2006). Finding text in natural scenes by figure-ground segmentation. Pattern Recognition, 2006. ICPR 2006. 18th International Conference on, 4, 113–118. IEEE.
Coughlan, J. ., & Yuille, A. L. (2000). The Manhattan world assumption: Regularities in scene statistics which enable Bayesian inference. NIPS, 845–851.