For pre-UCLA publications, please click here.

2009

  • Y. Chen, L. Zhu, A. L. Yuille, H. Zhang. Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation and Recognition using Knowledge Propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI. October 2009. [pdf]
  • A. Anderson, I. D Dinov, J. E Sherin, J. Quintana, A.L. Yuille, and M. Cohen. Classification of Spatially Unaligned fMRI Scans. NeuroImage. August 2009. [pdf]
  • D. Cremers, B. Rosenhahn, and A.L. Yuille (Eds). Statistical and Geometrical Approaches to Visual Motion Analysis. Spinger-Verlag Lecture Notes in Computer Science 5604. August 2009. [website]
  • S. Wu, H.J. Lu, A. Lee and A.L. Yuille. Motion Integration Using Competitive Priors. Statistical and Geometrical Approaches to Visual Motion Analysis. Spinger-Verlag Lecture Notes in Computer Science 5604. August 2009. [pdf]
  • I. Kokkinos and A.L. Yuille. HOP: Hierarchical Object Parsing. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2009. [pdf]
  • A.L. Yuille and S.F. Zheng. Compositional noisy-logical learning. Proceedings of the 26th Annual International Conference on Machine Learning. ICML. June 2009. [pdf]
  • L. Zhu, Y. Chen, A.L. Yuille. Learning a Hierarchical Deformable Template for Rapid Deformable Object Parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI. March 2009. [pdf]
  • L. Zhu, Y. Chen, A.L. Yuille. Unsupervised Learning of Probabilistic Grammar-Markov Models for Object Categories. IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI. January 2009. [pdf]

2008

  • S. Wu, H.J. Lu, A.L. Yuille. Model selection and parameter estimation in motion perception. Advances in Neural Information Processing Systems 21. NIPS. December 2008. [pdf]
  • L. Zhu, Y. Chen, Y. Lin, C. Lin, A.L. Yuille. Recursive Segmentation and Recognition Templates for 2D Parsing. Advances in Neural Information Processing Systems 21. NIPS. December 2008. [pdf]
  • L. Zhu, C. Lin, H. Huang, Y. Chen, A.L. Yuille. Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion. Proceedings of the European Conference on Computer Vision. ECCV. October 2008. [pdf]
  • H.J. Lu, A.L. Yuille, M. Liljeholm, P.W. Cheng, and K.J. Holyoak. Bayesian generic priors for causal learning. Psychological Review, vol. 115, no. 4, pp. 955-984. October 2008. [pdf]
  • H.J. Lu, R. Rojas, T. Beckers, and A.L. Yuille. Sequential causal learning in humans and rats. Proceedings of the 30th Annual Conference of the Cognitive Science Society. July 2008. [pdf]
  • I. Kokkinos and A. Yuille. Scale Invariance without Scale Selection. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2008. [pdf]
  • Y. Chen, L. Zhu, A.L. Yuille, H. Zhang. Unsupervised Learning of Probabilistic Object Models for Object Classification, Segmentation and Recognition. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2008. [pdf]
  • L. Zhu, Y. Chen, X. Ye, A.L. Yuille. Structure-Perceptron Learning of a Hierarchical Log-Linear Model. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2008. [pdf]
  • L. Zhu, Y. Chen, Y. Lu, C. Lin, A.L. Yuille. Max Margin AND/OR Graph Learning for Parsing the Human Body. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2008. [pdf]
  • J. J. Corso, A.L. Yuille, and Z. Tu. Graph-Shifts: Natural Image Labeling by Dynamic Hierarchical Computing. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2008. [pdf]
  • J. J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha, and A.L. Yuille. Efficient Multilevel Brain Tumor Segmentation with Integrated Bayesian Model Classification. IEEE Transactions on Medical Imaging, vol. 27, no. 5, pp. 629-640. May 2008. [pdf]
  • J. J. Corso, Z. Tu, and A.L. Yuille. MRF Labeling with a Graph-Shifts Algorithm. Proceedings of International Workshop on Combinatorial Image Analysis, pp. 172-184. April 2008. [pdf]
  • T.L. Griffiths and A.L. Yuille. A primer on probabilistic inference. In M.Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press. Pages 33-58. March 2008. [pdf]
  • S. Dube, J. J. Corso, A.L. Yuille, T. F. Cloughesy, S. El-Saden, and U. Sinha. Hierarchical Segmentation of Malignant Gliomas Via Integrated Contextual Filter Response. Image Processing. Edited by Reinhardt, Joseph M.; Pluim, Josien P. W. Proceedings of the SPIE, vol. 6914. February 2008. [pdf]
  • Z. Tu, S.F. Zheng, and A.L. Yuille. Shape Matching and Registration by Data-driven EM. Journal of Computer Vision and Image Understanding. CVIU. vol. 109, pp. 290-304. February 2008. [pdf]

2007

  • A.L. Yuille and H.J. Lu. The noisy-logical distribution and its application to causal inference. Advances in Neural Information Processing Systems 20. NIPS. December 2007. [pdf]
  • L. Zhu, Y. Chen, C. Lin, A.L. Yuille. Rapid Inference on a novel AND/OR graph: Detection, Segmentation and Parsing of Articulated Deformable Objects in Cluttered Backgrounds. Advances in Neural Information Processing Systems 20. NIPS. December 2007. [pdf]
  • J. Corso, A.L. Yuille, N. Sicotte, and A. Toga. Detection and Segmentation of Pathological Structures by the Extended Graph-Shifts Algorithm. Proceedings of Medical Image Computing and Computer Aided Intervention. MICCAI. October 2007. [pdf]
  • I. Kokkinos and A. Yuille. Unsupervised Learning of Object Deformation Models. Proceedings of IEEE International Conference on Computer Vision. ICCV. October 2007. [pdf]
  • A. L. Yuille, S. C. Zhu, D. Cremers, Y. Wang (Eds). Proceedings of the 6th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2007. Ezhou, China, August 27-29, 2007. Springer 2007. [website]
  • H.J. Lu, A.L Yuille, M. Liljeholm, P.W. Cheng, and K.J. Holyoak. Bayesian models of judgments of causal strength: A comparison. Proceedings of the 29th Annual Conference of the Cognitive Science Society. pp. 1241-1246. August 2007. [pdf]
  • J. J. Corso, Z. Tu, A. Yuille, and A. W. Toga. Segmentation of Sub-Cortical Structures by the Graph-Shifts Algorithm. Proceedings of Information Processing in Medical Imaging. pp. 183-197. July 2007. [pdf]
  • S.F. Zheng, Z. Tu, A. L. Yuille. Detecting Object Boundaries Using Low-, Mid-, and High-level Information. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2007. [pdf]
  • Z. Tu, S.F. Zheng, A.L. Yuille, A.L. Reiss, R.A. Dutton, A.D. Lee, A.M. Galaburda, I. Dinov, P.M. Thompson and A.W. Toga. Automated Extraction of the Cortical Sulci Based. on a Supervised Learning Approach. IEEE Transactions on Medical Imaging. Vol. 26. No. 4. pp. 541-552. April 2007. [pdf]
  • T.S. Lee and A.L. Yuille. Efficient Coding of Visual Scenes by Grouping and Segmentation: Theoretical Principles and Biological Evidence In the Bayesian Brain: Probabilistic Approaches to Neural Coding. Ed. K. Doya, S. Ishii, A. Pouget, and R.P.N. Rao. MIT Press. pp 145-188. January 2007. [pdf]

2006

  • L. Zhu, Y. Chen, and A.L. Yuille. Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing. Advances in Neural Information Processing Systems 19. NIPS. December 2006. [pdf]
  • Z. Tu, X. Chen, A.L. Yuille and S.C. Zhu. Image Parsing: Segmentation, Detection, and Recognition. In Towards Category-Level Object Recognition. Eds. J. Ponce, M. Hebert, C. Schmid, A. Zisserman. Springer LNCS 4170. pp 545-576. October 2006. [pdf]
  • J. J. Corso, E. Sharon, and A. L. Yuille. Multilevel Segmentation and Integrated Bayesian Model Classification with an Application to Brain Tumor Segmentation. Proceedings of Medical Image Computing and Computer Aided Intervention. MICCAI. vol. 2, pp. 790-798. October 2006. [pdf]
  • S.F. Zheng, Z. Tu, A. L. Yuille, A. L. Reiss, R. A. Dutton, A. D. Lee, A. M. Galaburda, P. M. Thompson, I. D. Dinov, A. W. Toga. A Learning Based Algorithm for Automatic Extraction of the Cortical Sulci. Proceedings of Medical Image Computing and Computer Aided Intervention. MICCAI. vol. 1, pp. 695-703. October 2006. [pdf]
  • H.J. Lu, A.L Yuille, M. Liljeholm, P.W. Cheng, and K.J. Holyoak. Modeling causal learning using Bayesian generic priors on generative and preventive powers. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp. 519-524. July 2006. [pdf]
  • T.L. Griffiths and A.L. Yuille. A primer on probabilistic inference. In Trends in Cognitive Sciences. Supplement to special issue on Probabilistic Models of Cognition, vol 10, no. 7. July 2006. [pdf]
  • A.L. Yuille and D. Kersten. Vision as Bayesian Inference: Analysis by Synthesis? In Trends in Cognitive Neuroscience, vol. 10, no. 7, pp. 301-308. July 2006. [pdf]
  • N. Chater, J. Tenenbaum, A.L. Yuille. Probabilistic models of cognition: Where next? In Trends in Cognitive Neuroscience, vol. 10, no. 7, pp. 292-293. July 2006. [pdf]
  • N. Chater, J. Tenenbaum and A.L. Yuille. Probabilistic Models of Cognition: Conceptual Foundations. In Trends in Cognitive Neuroscience, vol. 10, no. 7, pp. 287-291. July 2006. [pdf]
  • B. Rokers, A.L. Yuille and Z. Liu. The perceived motion of a stereokinetic stimulus. Vision Research, vol. 46, no. 15, pp. 2375-87. July 2006. [pdf]
  • I. Kokkinos, P. Maragos, A. L. Yuille. Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2006. [pdf]

2005

  • H.J. Lu and A.L. Yuille. Ideal Observers for Detecting Human Motion: Correspondence Noise. Advances in Neural Information Processing Systems 18. NIPS. December 2005. [pdf]
  • A.L. Yuille. Augmented Rescorla-Wagner and Maximum Likelihood Estimation. Advances in Neural Information Processing Systems 18. NIPS. December 2005. [pdf]
  • L. Zhu, A.L. Yuille. A Hierarchical Compositional System for Rapid Object Detection. Advances in Neural Information Processing Systems 18. NIPS. December 2005. [pdf]
  • A. Rangarajan, B. C. Vemuri, A. L. Yuille (Eds). Proceedings of the 5th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2005. St. Augustine, FL, USA, November 9-11, 2005, Proceedings Springer 2005. 
  • M. Rosen-Zvi, M. I. Jordan, A. L. Yuille. The DLR Hierarchy of Approximate Inference. UAI. pp. 493-500. July 2005. [pdf]
  • Z. Tu, X. Chen, A.L. Yuille, S.C. Zhu. Image Parsing: Unifying Segmentation, Detection, and Recognition. International Journal of Computer Vision. IJCV. vol. 63, no. 2, pp. 113-140. July 2005. [pdf]
  • X. Chen and A.L. Yuille. A Time-Efficient Cascade for Real Time Object Detection. 1st International Workshop on Computer Vision Applications for the Visually Impaired. In association with CVPR 2005. June 2005. [pdf]

2004

  • A.L. Yuille. The Rescorla-Wagner Algorithm and Maximum Likelihood Estimation of Causal Parameters. Advances in Neural Information Processing Systems 17. NIPS. December 2004. [pdf]
  • A.L. Yuille. The Convergence of Contrastive Divergences. Advances in Neural Information Processing Systems 17. NIPS. December 2004. [pdf]
  • D. Kersten, P. Mamassian and A.L. Yuille. Object Perception as Bayesian Inference. Annual Review of Psychology, vol. 555, pp 271-304. 2004. [pdf]
  • X. Chen and A.L. Yuille. AdaBoost Learning for Detecting and Reading Text in City Scenes. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2004. [pdf]
  • A. Barbu and A.L. Yuille. Motion Estimation by Swendsen-Wang Cuts. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2004. [pdf]
  • Z. Tu and A.L. Yuille. Shape Matching and Recognition: Using Generative Models and Informative Features. Proceedings of the European Conference on Computer Vision. ECCV. vol. 3, pp 195-209, May 2004. [pdf]

2003

  • J.M. Coughlan and A.L. Yuille. A Large Deviation Theory Analysis of Bayesian Tree Search. In Mathematical Methods in Computer Vision, Eds. P. Olver and A. Tannenbaum, IMA Volumes in Mathematics and its Applications, vol. 133, pp 1-17, Spinger, 2003. [pdf]
  • A.L. Yuille, F. Fang, P. Schrater and D. Kersten. Human and Ideal Observers for Detecting Image Curves. Advances in Neural Information Processing Systems 16. NIPS. December 2003. [pdf]
  • A. Rangarajan, J.M. Coughlan and A.L. Yuille. A Bayesian Network for Relational Shape Matching. Proceedings of International Conference on Computer Vision. ICCV. October 2003. [pdf]
  • Z. Tu, X. Chen, A.L. Yuille and S.C. Zhu. Image Parsing: Segmentation, Detection, and Recognition. Proceedings of International Conference on Computer Vision. ICCV. October 2003. [pdf]
  • D. Cremers and A.L. Yuille. A Generative Model Based Approach to Motion Segmentation. In B. Michaelis and G. Krell (Eds.). German Conference on Pattern Recognition (DAGM), Springer LNCS vol. 2781, pp 313-320, September 2003. [pdf]
  • A.L. Yuille, J.M. Coughlan, and S. Konishi. The Generic Viewpoint Assumption and Planar Bias. IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI. Vol. 25, No. 8, August 2003. [pdf]
  • J.M. Coughlan and A.L. Yuille. Manhattan World. Neural Computation, Vol. 15, No. 5, pp 1063-1088. May 2003. [pdf]
  • D. Kersten and A.L. Yuille. Bayesian Models of Object Perception. Current Opinion in Neurobiology, Vol. 13, pp 1-9. April 2003. [pdf]
  • A.L. Yuille and A. Rangarajan. The Concave-Convex Procedure (CCCP). Neural Computation, Vol. 15, No. 4, pp 915-936. April 2003. [pdf]
  • S.M. Konishi, A.L. Yuille, and J.M. Coughlan. A Statistical Approach to Multi-Scale Edge Detection. Image and Vision Computing (IVC), Special issue on Generative-Model Based Vision, Vol. 21, No. 1, pp 37-48, January 2003. [pdf]
  • J.M. Coughlan and A.L. Yuille. Algorithms from Statistical Physics for Generative Models of Images. Image and Vision Computing (IVC), Special issue on Generative-Model Based Vision, Vol. 21, No. 1, pp 29-36, January 2003. [pdf]
  • S. M. Konishi, A.L. Yuille, J.M. Coughlan and S.C. Zhu. Statistical Edge Detection: Learning and Evaluating Edge Cues. IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI. Vol. 25, No. 1, pp 57-74. January 2003. [pdf]
  • A.L. Yuille, J. M. Coughlan, and S. Konishi. The KGBR Viewpoint-Lighting Ambiguity. Journal of the American Optical Society. Vol.20, No. 1, pp 24-31. January 2003. [pdf]