James Coughlan

Coughlan, J., & Manduchi, R. (2009). Functional assessment of a camera phone-based wayfinding system operated by blind and visually impaired users. International Journal On Artificial Intelligence Tools, 18, 379–397.

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.

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.