Selected Publications

2014

  • Xianjie Chen, Alan Yuille. Parsing Occluded People by Flexible Compositions. arXiv:1412.1526 [cs.CV]. [arXiv]
  • Xianjie Chen, Alan Yuille. Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations. Advances in Neural Information Processing Systems (NIPS). 2014. [pdf] [poster][project]
  • Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, and Alan L. Yuille. Explain Images with Multimodal Recurrent Neural Networks. [arXiv:1410.1090]
  • Wenhao Lu, Xiaochen Lian, A.L. Yuille. Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency. British Machine Vision Conference (BMVC). 2014. [pdf]
  • Jian Dong, Qiang Chen, Shuicheng Yan, and A.L. Yuille.  Towards Unified Object Detection and Segmentation.  European Conference on Computer Vision (ECCV). 2014. [pdf]
  • B. Bonev and A.L. Yuille. A Fast and Simple Algorithm for Producing Candidate Regions.  European Conference on Computer Vision (ECCV). 2014.
  • Junhua Mao, Jun Zhu, and Alan L. Yuille. An Active Patch Model for Real World Texture and Appearance Classification. European Conference on Computer Vision (ECCV). 2014. [pdf | project]
  • G. Papandreou and A.L. Yuille. Perturb-and-MAP Random Fields: Reducing Random Sampling to Optimization, with Application in Computer Vision. MIT press volume on “Advanced StructuredPrediction”. Ed.  S. Nowozin, P. V. Gehler, J. Jancsary, and C. H. Lampert. To appear. 2014.
  • D. Kersten and A.L. Yuille. Inferential Models of the Visual Cortical Hierarchy. Brown The New Cognitive Neurosciences, 5th Edition. Gazzaniga (Ed.) 2014.
  • A. Anderson, P.K. Douglas, W.T. Kerr, V..S. Haynes, A.L. Yuille, J. Xie, Y.N. Wu, J.A. Brown, and M.S. Cohen. Non-negative Matrix Factorization of Multimodal MRO, fMRI, and Phenotypic Data reveals Differenital Changes in Default Mode Subnetworks in ADHD. Neuroimage 2014. [pdf]
  • G. Guo, Y. Wang, T. Jiang, A.L. Yuille, F. Fang, and W. Gao. A Shape Reconstructability measure of Object Part Importance with Applications to Object Detection and Localization. IJCV 2014. [pdf]
  • J. Ma, J. Zhai, J. Tian, A.L. Yuille, and Z. Tu. Robust Point Matching via Vector Field Consensus. Transactions in Image Processing 2014. [pdf]
  • A.L. Yuille and J. Luo. Guest Editorial: Geometry, Lighting, Motion, and Learning. IJCV 2014. [pdf]
  • George Papandreou, Liang-Chieh Chen, and Alan L. Yuille. Modeling Image Patches with a Generic Dictionary of Mini-Epitomes. CVPR 2014. [pdf]
  • Y. Li, X. Hou, C. Koch, J.M. Rehg, and A.L. Yuille. The Secrets of Salient Object Segmentation. CVPR. 2014. pdf
  • Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nan-Gyu Cho, Seong-Whan Lee,Sanja Fidler,Raquel Urtasun, and Alan Yuille. The Role of Context for Object Detection and Semantic Segmentation in the Wild. CVPR 2014. [pdf] [errata] [dataset]
  • Xianjie Chen, Roozbeh Mottaghi, Xiaobai Liu, Sanja Fidler, Raquel Urtasun and Alan Yuille. Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts. CVPR. 2014. [pdf][dataset]
  • Y. Zhu, Y. Zhang, and A.L. Yuille. Single Image Super-resolution using Deformable Patches. CVPR. 2014. pdf
  • Liang-Chieh Chen, Sanja Fidler, Alan L. Yuille, and Raquel Urtasun. Beat the MTurkers: Automatic Image Labeling from Weak 3D Supervision . CVPR 2014. [pdf]
  • Chunyu Wang, Yizhou Wang, Zhouchen Lin, Alan L.Yuille and Wen Gao. Robust 3D Human Pose Estimation from Single Images. CVPR2014. [pdf]
  • Weichao Qiu, Xinggang Wang, Xiang Bai, Alan Yuille, and Zhuowen Tu. Scale-Space SIFT Flow. Winter Conference on Applications of Computer Vision (WACV), 2014 [pdf]

2013

  • D. Kersten and A.L. Yuille. Bayesian Inference and Beyond. The New Visual Neurosciences. John S. Werner and Leo M. Chalupa
    (Editors) MIT Press. Cambridge MA. 2013. [pdf]
  • Liang-Chieh Chen, George Papandreou, and Alan L. Yuille. Learning a Dictionary of Shape Epitomes with Applications to Image Labeling . ICCV 2013. [pdf]
  • A. L. Yuille and R. Mottaghi. Complexityof Representation and Inference in Compositional Models with Part Sharing.International Conference on Learning Representations (ICLR). 2013. [pdf]
  • Chunyu Wang, Yizhou Wang, Zhouchen Lin and Alan L.Yuille. An approach to pose-based action recognition. CVPR 2013. [pdf ]
  • J. Ma, Z. Zhao, J. Tian, Z. Tu, and A.L. Yuille. Robust Nonrigid Point Set Registration Using the L2-Minimizung Estimate. CVPR2013, [pdf]
  • X. Hou, A.L. Yuille, and C. Koch. Boundary detection benchmarking: beyond F-measures. CVPR 2013. [pdf]
  • X. Liu, L. Liu, A.L. Yuille. MsLRR: Segment Images via Internal Replication Prior. CVPR 2013. [pdf]
  • S. Fidler, R. Mottaghi, A.L. Yuille, and R. Urtasun. Bottom-Up Segmentation for Top-Down Detection. CVPR 2013. [pdf]

2012

  • A.L. Yuille and H.H. Buelthoff. Where’s the Action? Action as an innate bias for visual learning. Commentary. proceedings of the
    National Academy of Sciences. (PNAS). October. 2012. [pdf]
  • A.L. Yuille. Computer Vision needs a Core and Foundation. Opinion Paper. Image and Vision Computing. Accepted . June. 2012. [pdf]
  • N.-G. Cho,  A.L. Yuille, and S. -W. Lee. Adaptive Self-Occlusion Reasoning for 3D Human Pose Tracking from Accepted. Monocular Image Sequences.  Pattern Recognition. June. 2012. [pdf]
  • A.L. Yuille and X. He. Probabilistic Models of Vision and Max-Margin Methods. Frontiers of Electrical and Electornic
    Engineering. Vol. 7, Number 1. March. 2012. [pdf]
  • Y. Nishihara, X. Ye, and A.L. Yuille. A family of CCCP Algorithms with minimize the TRW Free Energy. New Generation Computing. 30: 3-16. January. 2012. [pdf]
  • L. Zhu, Y. Chen, Y. Lin, C. Lin, and A.L. Yuille. Recursive Segmentation and Recognition Templates for Image Parsing. IEEE Trans. Pattern Anal. Mach. Intell. 34(2): 359-371. January. 2012. [pdf]

2011

  • G. Papandreou and A.L. Yuille. Peturb and Map: Using Discrete Optimization to Learn and Sample from Energy Models. International Conference on Computer Vision (ICCV). November. 2011. [pdf]
  • A. Yuille. Towards a Theory of Compositional Learning and Encoding of Objects. 1st IEEE Workshop in Information Theory in Computer Vision and Pattern Recognition. ICCV. November. 2011. [pdf]
  • G. Papandreou and A.L. Yuille. Efficient Variational Inference in Large-scale Bayesian Compressed Sensing. 1st IEEE Workshop in Information Theory in Computer Vision and Pattern Recognition. ICCV. November. 2011. [pdf]
  • C. Guo, Y. Wang, Y. Jiang, A.L. Yuille, and W. Gao. Computing Importance of 2D Contour parts by Reconstructability. 1st IEEE Workshop in Information theory in Computer Vision and Pattern Recognition. ICCV. Novermber. 2011. [pdf]
  • R. Mottaghi and A.L. Yuille. A compositional approach to learning part-based models for single and multi-view object detection. #dRR-11 workshop. ICCV. November. 2011. [pdf]
  • X. Ye and A.L. Yuille. Learning a Dictionary of Deformable patches using GPUs. Workshop on GPU’s in Computer Vision Applications. ICCV. November.  2011. [pdf]
  • N-M Cho, A.L. Yuille, and S-W Lee. Nonflat Observation Model and Adpative Depth Order Estimation for 3D Human Pose Tracking. First Asian Conference on pattern Recognition. Beijing. November. 2011. [pdf]
  • L. Zhu, Y. Chen, and A.L. Yuille. Recursive Compositional Models for Vision: Description and Review of Previous work. Journal of Mathematical Imaging and Vision. 41(1-2): 122-146. Spetember 2011. [pdf]
  • P-H. Lee, J.J. Lee, S-W Lee, A.L. Yuille and C. Koch. Adaboost for text Detection in Natural Scences. Proceeding of International Conference on Document Analysis and Recognition. PP 429-434. Sepetember. 2011. {pdf]
  • A. Yuille. Belief Propagation, Mean Field, and Bethe Approximations. In Advances in Markov Random Fields for  Vision and Image Processing. Ed.s A. Blake, P. Kohli, and C. Rother. MIT Press September. 2011. [pdf]
  • A, Anderson, J. Bramen, P. Douglas, A. Lenartowicz, A. Cho, C. Culbertson, A.L. Brody, A.L. Yuille, and M.S. Cohen. Large Sample Group Independent Component Analysis of Functional Magemtic Resonance Imaging using Anatomical atlas based reduction and bootstrapped clustering. InternationalJournal of Imaging Systems and Technology. Special Issue on Brain Mapping and Neuroimaging. 21(2). June 2011. [pdf]
  • I. Kokkinos and A.L. Yuille. Inference and Learning with Hierarchical Compositional Models. International Journal of Computer Vision. Vol. 93(2):201-225. June. 2011. [pdf]
  • L. Zhu, Y. Chen, and A.L. Yuille. Max-Margin AND/OR graph learning for parsing the human body. International Journal of Computer Vision. 93: 1-21. May. 2011. [pdf]

2010

  • P.K. Douglas, S. Harris, A.L. Yuille, and M.S. Cohen. Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief versus disbelief. Neuroimage. Nov. 2010. [pdf]
  • S. Zheng, A.L.Yuille, and Z. Tu. DetectingObject Boundaries Using Low-, Mid-, and High-Level Information,Journalof Computer Vision and Image Understanding. Vol 114. No. 10, pp 1055-1067. Oct. 2010. [pdf]
  • A.L. Yuille. An Information Theoretic Perspective on Computer Vision. Recent Advances on Information Theoretical Methods. Frontiers of Electrical and Electronic Engineering. Eds. Lei Xu. 6(1). August 2010. [pdf]
  • H. Lu, T. Lin, A. Lee, L. Vese, A.L. Yuille. Functionalform of Motion Priors in Human Motion Perception. NIPS. December. 2010. [pdf]
  • S. Wu, X. He, H. Lu, A.L. Yuille. A Unified model of short-range and long-range motion perception. In NIPS. December. 2010. [pdf]
  • G. Papandreou, A.L. Yuille. Gaussian Sampling by Local Perturbation. In NIPS. December. 2010. [pdf]
  • X. He, A.L. Yuille. Occlusion Boundary Detection using Pseudo-Depth. In ECCV. September. 2010. [pdf]
  • Y. Chen, L. Zhu, A.L. Yuille. Active Mask Hierarchies for Object Detection.  In ECCV. September. 2010. [pdf]
  • L. Zhu, Y. Chen, A. Torrable, W. Freeman, A.L. Yuille. Part and Appearance Sharing: Recursive Compositional Models for Multi-View Multi-Object Detection. In CVPR. June. 2010. [pdf]
  • L. Zhu, Y. Chen, A.L. Yuille, W. Freeman. Latent Hierarchical Structure Learning for Object Detection. In CVPR. June 2010. [pdf]

2009

  • H. Lu, M. Weiden, A.L. Yuille. Modeling the spacing effect in sequential category learning. In NIPS. Dec. 2009. [pdf]
  • 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. IEEETransactions 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. RecursiveSegmentation 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. UnsupervisedStructure 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. PsychologicalReview, 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. UnsupervisedLearning 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-PerceptronLearning 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 Inthe 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. Advancesin 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. MultilevelSegmentation and Integrated Bayesian Model Classification with an Application to Brain Tumor Segmentation.Proceedingsof 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. Probabilisticmodels 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. ProbabilisticModels 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. Advancesin 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). Proceedingsof 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. InternationalJournal 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. 1stInternational 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. Proceedingsof 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. InB. 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]
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