140 Governors Drive, Office 248
Amherst, MA 01003

Phone: (413) 545-2993
E-mail: elm at cs.umass.edu
Computer Vision Lab

News:

• New, much tighter confidence intervals for the mean! It is far tighter than so-called envelope methods and uses the same weak set of assumptions. For the first time, it gives useful confidence intervals even when the sample size is small (less than 30).

• Cheng Gu, Erik Learned-Miller, Daniel Sheldon, Guillermo Gallego, and Pia Bideau.
  The spatio-temporal Poisson point process: A simple model for the alignment of event camera data.
  International Conference on Computer Vision (ICCV), 10 pages, 2021.
  [pdf]

• My Phan, Philip Thomas, and Erik Learned-Miller.
  Towards practical mean bounds for small samples.
  Proceedings of the International Conference on Machine Learning (ICML), 2021.
  [pdf][supplementary material]

• Cheng Gu, Erik Learned-Miller, Daniel Sheldon, Guillermo Gallego, and Pia Bideau.
  The spatio-temporal Poisson point process: A simple model for the alignment of event camera data.
  ArXiv preprint, 14 pages, Jun., 2021.
  [arXiv]

• Chen Qu, Hamed Zamani, Liu Yang, W. Bruce Croft, and Erik Learned-Miller.
  Passage retrieval for outside-knowledge visual question answering.
  Special Interest Group on Information Retrieval (SIGIR, Short Paper), 2021.
  [pdf]

• Huaizu Jiang and Erik Learned-Miller.
  DCVNet: Dilated cost volume networks for fast optical flow.
  ArXiv preprint, 17 pages, March 31, 2021.
  [pdf]

• Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik Learned-Miller, Emma Brunskill, and Philip S. Thomas.
  Universal off-policy evaluation.
  ArXiv preprint, 25 pages, April 26, 2021.
  [arXiv]

• Alireza Sedghi, Lauren J. O'Donnell, Tina Kapur, Erik Learned-Miller, Parvin Mousavi, and William M. Wells III.
  Image registration: Maximum likelihood, minimum entropy and deep learning.
  Medical Image Analyis, Volume 69, 2021.
  [link]

• Zitian Chen, Subhransu Maji, and Erik Learned-Miller.
  Shot in the Dark: Few-Shot Learning with No Base-Class Labels.
  arXiv:2010.02430, 2020.
  [pdf]

• Zitian Chen, Zhiqiang Shen, Jiahui Yu, and Erik Learned-Miller.
  Cross-supervised object detection.
  arXiv report: 2006.15056, 2020.
  [pdf]

• Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma, Subhransu Maji, Rui Wang, Evangelos Kalogerakis, Liangliang Cao, and Erik Learned-Miller.
  Label-efficient learning on point clouds using approximate convex decompositions.
  European Conference on Computer Vision (ECCV), 2020.
  [pdf]

• Aruni RoyChowdhury, Xiang Yu, Kihyuk Sohn, Erik Learned-Miller, and Manmohan Chandraker.
  Improving recognition with unlabeled faces in the wild.
  European Conference on Computer Vision (ECCV), 2020.
  [pdf]

• Zhipeng Tang, Fabien Delattre, Pia Bideau, Mark Corner, and Erik Learned-Miller.
  C-14: Assured timestamps for drone videos.
  The 26th Annual International Conference on Mobile Computing and Networking (MobiCom), 2020.
  [pdf]

• Erik Learned-Miller, Vicente Ordonez, Jamie Morgenstern, and Joy Buolamwini.
  Facial Recognition Technologies in the Wild: A Call for a Federal Office.
  56 pages, May 29, 2020.
  [pdf]

• Joy Buolamwini, Vicente Ordonez, Jamie Morgenstern, and Erik Learned-Miller.
  Facial Recognition Technologies: A Primer.
  18 pages, May 29, 2020.
  [pdf]

• Huaizu Jiang, Ishan Misra, Marcus Rohrbach, Erik Learned-Miller, and Xinlei Chen.
  In Defense of Grid Features for Visual Question Answering.
  Computer Vision and Pattern Recognition (CVPR), 10 pages, 2020.
  [pdf]

• Huaizu Jiang, Deqing Sun, Varun Jampani, Zhaoyang Lv, Erik Learned-Miller, and Jan Kautz.
  SENSE: A shared encoder network for scene-flow estimation.
  International Conference on Computer Vision (ICCV), 10 pages, 2019.
  [pdf]

• Erik Learned-Miller and Philip S. Thomas.
  A new confidence interval for the mean of a bounded random variable.
  ArXiv preprint, 27 pages, May 15, 2019.
  [arXiv]

• Philip Thomas and Erik Learned-Miller.
  Concentration inequalities for conditional value at risk.
  Proceedings of the International Conference on Machine Learning (ICML), 2019.
  [pdf][erratum]

• Aruni RoyChowdhury, Prithvijit Chakrabarty, Ashish Singh, SouYoung Jin, Huaizu Jiang, Liangliang Cao, and Erik Learned-Miller.
  Automatic adaptation of object detectors to new domains using self-training.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 11 pages, 2019.
  [pdf]

• Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik Learned-Miller, and Jan Kautz.
  Pixel Adaptive Convolutional Neural Networks.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 10 pages, 2019.
  [pdf]

• SouYoung Jin*, Aruni RoyChowdhury*, Huaizu Jiang, Ashish Singh, Aditya Prasad, Deep Chakraborty, and Erik Learned-Miller.
  Unsupervised hard example mining from videos for improved object detection.
  European Conference on Computer Vision (ECCV), 18 pages, 2018.
  * = Equal Contribution
  [pdf]

• Huaizu Jiang, Gustav Larsson, Michael Maire, Greg Shakhnarovich, and Erik Learned-Miller.
  Self-supervised relative depth learning for urban scene understanding.
  European Conference on Computer Vision (ECCV), 16 pages, 2018.
  [pdf]

• Hamed Zamani, Mostafa Dehghani, Bruce Croft, Erik Learned-Miller, and Jaap Kamps.
  From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing.
  Conference on Information and Knowledge Management (CIKM) , 2018.
  [pdf]

• Li Yang Ku, Jonathan Rogers, Philip Strawser, Julia Badger, Erik Learned-Miller and Rod Grupen.
  A framework for dexterous manipulation.
  IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 8 pages, 2018.
  [pdf]

• Li Yang Ku, Erik Learned-Miller and Rod Grupen.
  Integration of robotic perception, action, and memory.
  In Workshop on Computational Models of Affordance, at Robotics: Science and Systems, 8 pages, June, 2018.
  [pdf]

• Li Yang Ku, Scott Jordan, Julia Badger, Erik Learned-Miller and Rod Grupen.
  Learning to use a ratchet by modeling spatial relations in demonstrations.
  In Workshop on Learning from Demonstrations for High Level Robotics Tasks, at Robotics: Science and Systems, 9 pages, June, 2018.
  [pdf]

• Pia Bideau, Aruni RoyChowdhury, Rakesh Menon, and Erik Learned-Miller.
  The best of both worlds: Combining CNNs and geometric constraints for hierarchichal motion segmentation.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 10 pages, 2018.
  [pdf]

• Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz.
  Super SloMo: High quality estimation of multiple intermediate frames for video interpolation.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 12 pages, 2018.
  [pdf]

• Keen Sung, Marc Liberatore, Joydeep Biswas, Erik Learned-Miller, Brian Levine.
  Server-side traffic analysis reveals mobile location information over the internet.
  IEEE Transactions on Mobile Computing, 2018.
  [IEEE Explore]

• Hamed Bonab, Hamed Zamani, Erik Learned-Miller, and James Allan.
  Citation worthiness of sentences in scientific reports.
  Special Interest Group on Information Retrieval (SIGIR, Short Paper), 2018.
  [pdf]

• Huaizu Jiang, Erik Learned-Miller, Gustav Larsson, Michael Maire, and Greg Shakhnarovich.
  Self-supervised depth learning for urban scene understanding.
  ArXiv preprint, 14 pages, Dec., 2017.
  [arXiv]

• Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, and Jan Kautz.
  Super SloMo: High quality estimation of multiple intermediate frames for video interpolation.
  ArXiv preprint, 12 pages, Nov., 2017.
  [arXiv]

• Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum.
  Active bias: Training a more accurate neural network by emphasizing high variance samples.
  In Neural Information Processing Systems (NIPS) , 2017.
  [arXiv]

• SouYoung Jin, Hang Su, Chris Stauffer, and Erik Learned-Miller.
  End-to-end face detection and cast grouping in movies using Erdos-Renyi clustering.
  International Conference on Computer Vision (ICCV), 10 pages, 2017.
  [pdf] [Project page]

• Li Yang Ku, Rod Grupen, Erik Learned-Miller.
  Associating grasp configurations with hierarchical features in convolutional neural networks.
  IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 8 pages, 2017.
  [pdf]

• Derui Liu, Nina Zehfroosh, Brandon L. Hancock, Kevin Hines, Wenjuan Fang, Maria Kilfoil, Erik Learned-Miller, Karen A. Sanguinet, Lori S. Goldner, Tobias I Baskin.
  Imaging cellulose synthase motility during primary cell wall synthesis in the grass Brachypodium distachyon.
  Scientific Reports, 12 pages, Nov., 2017.
  [pdf]

• Huaizu Jiang and Erik Learned-Miller.
  Face detection with the Faster R-CNN.
  IEEE Conference on Automatic Face and Gesture Recognition (FandG), 6 pages, 2017.
  [pdf]

• Li Yang Ku, Rod Grupen, Erik Learned-Miller.
  An Aspect Representation for Object Manipulation Based on Convolutional Neural Networks.
  IEEE International Conference on Robotics and Automation (ICRA), 7 pages, 2017.
  [pdf]

• Pia Bideau and Erik Learned-Miller.
  A detailed rubric for motion segmentation.
  ArXiv preprint, 16 pages, 2016.
  [arXiv] [Project Page]

• Huaizu Jiang and Erik Learned-Miller.
  Face detection with the Faster R-CNN.
  ArXiv preprint arXiv:1606.03473, 6 pages, 2016.
  [pdf]

• Pia Bideau and Erik Learned-Miller.
  It's moving! A probabilistic model for causal motion segmentation in moving camera videos.
  European Conference on Computer Vision (ECCV), 16 pages, 2016.
  [pdf]

• Pia Bideau and Erik Learned-Miller.
It's moving! A probabilistic model for causal motion segmentation in moving camera videos.
ArXiv preprint, 22 pages, 2016.
[arXiv] [Project Page] [Code]
• Erik Learned-Miller, Gary B. Huang, Aruni RoyChowdhury, Haoxiang Li, and Gang Hua.
  Labeled Faces in the Wild: A Survey.
  In Advances in Face Detection and Facial Image Analysis,, edited by Michal Kawulok, M. Emre Celebi, and Bogdan Smolka, Springer, pages 189-248, 2016.
  [Springer Page] [Draft pdf]

• M. Savva, F. Yu, H. Su, M. Aono, B. Chen, D. Cohen-Or, W. Deng, H. Su, S. Bai, X. Bai, N. Fish,
  J. Han, E. Kalogerakis, E. G. Learned-Miller, Y. Li, M. Liao, S. Maji, A. Tatsuma, Y. Wang, N. Zhang, and Z. Zhou.
  SHREC’16 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55.
  In Eurographics Workshop on 3D Object Retrieval, J. Jorge and M. Lin, editors, 11 pages, 2016.
  [pdf]

• Aruni RoyChowdhury, Tsung-Yu Lin, Subhransu Maji, and Erik Learned-Miller.
  One-to-many face recognition with bilinear CNNs.
  Winter Conference on Applications of Computer Vision (WACV), 2016.
  [pdf]

• Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller.
  Multi-view convolutional neural networks for 3D shape recognition.
  International Conference on Computer Vision (ICCV), 9 pages, 2015.
  [project] [pdf]

• Li Yang Ku, Dirk Ruiken, Erik Learned-Miller and Rod Grupen.
  Error detection and surprise in stochastic robot actions.
  International Conference on Humanoid Robots, 2015.
  [pdf]

• Li Yang Ku, Erik Learned-Miller and Rod Grupen.
  Modeling objects as aspect transition graphs to support manipulation.
  International Symposium of Robotics Research (ISRR), 16 pages, 2015.
  [pdf]

• Aruni RoyChowdhury, Tsung-Yu Lin, Subhransu Maji, and Erik Learned-Miller.
  Face identification with bilinear CNNs.
  ArXiv preprint, 13 pages, 2015.
  [arXiv]

• Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller.
  Multi-view convolutional neural networks for 3D shape recognition.
  ArXiv preprint, 12 pages, 2015.
  [pdf] [arXiv]

• Laura Sevilla-Lara, Deqing Sun, Erik G. Learned-Miller and Michael J. Black.
  Optical flow estimation with channel constancy.
  European Conference on Computer Vision (ECCV), 2014.
  [pdf]

• Li Yang Ku, Shiraj Sen, Erik Learned-Miller and Rod Grupen.
  Action-based models for belief-space planning.
  In Workshop on Information-Based Grasp and Manipulation Planning, at Robotics: Science and Systems, 6 pages, July, 2014.
  [pdf]

• Li Yang Ku, Shiraj Sen, Erik Learned-Miller and Rod Grupen.
  The aspect transition graph: An affordance-based model.
  In Second Workshop on Affordances: Visual Perception of Affordances and Functional Visual Primitives for Scene Analysis, at the European Conference on Computer Vision, 7 pages, September, 2014.
  [pdf]

• Gary B. Huang and Erik Learned-Miller.
  Labeled Faces in the Wild: Updates and new reporting procedures.
  UMass Amherst Technical Report UM-CS-2014-003, 5 pages, 2014.
  [pdf]

• Andrew Kae, Benjamin Marlin, and Erik Learned-Miller.
  The shape-time random field for semantic video labeling.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
  [pdf]

• Manjunath Narayana, Allen Hanson, and Erik Learned-Miller.
  Background subtraction: separating the modeling and the inference.
  Machine Vision and Applications (MVAP), vol. 25, no. 3, pp. 1163-1174, 2014.
  [Springer Publication Page]

• Manjunath Narayana, Allen Hanson, and Erik Learned-Miller.
  Coherent motion segmentation in moving camera videos using optical flow orientations.
  International Conference on Computer Vision (ICCV), 2013.
  [pdf] [Supplementary Material]

• Andrew Kae*, Kihyuk Sohn*, Honglak Lee and Erik Learned-Miller.
  Augmenting CRFs with Boltzmann machine shape priors for image labeling.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
  *The first and second authors made equal contributions and should be considered co-first authors.
  [pdf]

• Benjamin Mears, Laura Sevilla Lara and Erik Learned-Miller.
  Adaptive kernels for improved local patch alignment.
  Proceedings of the British Machine Vision Conference (BMVC), 2013.
  [pdf]

• Jacqueline Feild, Erik Learned-Miller and David A. Smith.
  Using a probabilistic syllable model to improve scene text recognition.
  Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2013.
  [pdf]

• Jacqueline Feild and Erik Learned-Miller.
  Improving open-vocabulary scene text recognition.
  Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2013.
  [pdf]

• Yahan Zhou, Jacqueline Feild, Rui Wang, and Erik Learned-Miller.
  Scene text segmentation via inverse rendering.
  Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2013.
  [pdf]

• Mastooreh (Negin) Salajegheh, Yue Wang, Anxiao (Andrew) Jiang, Erik Learned-Miller and Kevin Fu.
  Half-wits: Software techniques for low-voltage probabilistic storage on microcontrollers with NOR flash memory.
  ACM Transactions on Embedded Computing Systems. Special Issue on Probabilistic Embedded Computing, Vol. 12, No. 2s, Article 91, 25 pages, May, 2013.
  [pdf]

• Hamed Soroush, Keen Sung, Erik Learned-Miller, Brian Neil Levine, and Marc Liberatore.
  Disabling GPS is not enough: Cellular location leaks over the Internet.
  Privacy Enhancing Technologies Symposium (PETS), 2013.
  [pdf]

• Gary B. Huang, Marwan Mattar, Honglak Lee, and Erik Learned-Miller.
  Learning to align from scratch.
  In Neural Information Processing Systems (NIPS) , 2012.
  [pdf]

• Jacqueline Feild and Erik Learned-Miller.
  Scene text recognition with bilateral regression.
  UMass Amherst Technical Report UM-CS-2012-021, 15 pages, 2012.
  [pdf]

• Manjunath Narayana, Allen Hanson, and Erik Learned-Miller.
  Improvements in joint domain-range modeling for background subtraction.
  Proceedings of the British Machine Vision Conference (BMVC), 2012.
  [pdf]

• Marwan Mattar, Allen Hanson, and Erik Learned-Miller.
  Unsupervised joint alignment and clustering using Bayesian nonparametrics.
  Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2012.
  [pdf]

• Gary B. Huang, Honglak Lee, and Erik Learned-Miller.
  Learning hierarchical representations for face verification.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
  [pdf]

• Laura Sevilla Lara and Erik Learned-Miller.
  Distribution fields for tracking.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
  [pdf]

• Manjunath Narayana, Allen Hanson, and Erik Learned-Miller.
  Background modeling using adaptive pixelwise kernel variances in a hybrid feature space.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
  [pdf]

• Gary B. Huang, Andrew Kae, Carl Doersch, and Erik Learned-Miller.
  Bounding the probability of error for high precision optical character recognition.
  Journal of Machine Learning Research (JMLR), Volume 13, pp. 363-387, 2012.
  [pdf]

• Laura Sevilla Lara and Erik Learned-Miller.
  Distribution Fields.
  UMass Amherst Technical Report UM-CS-2011-027, 18 pages, 2011.
  [pdf]

• Gang Hua, Ming-Hsuan Yang, Erik Learned-Miller, Yi Ma, Matthew Turk, David J. Kriegman, and Thomas S. Huang.
  Introduction to the special section on real-world face recognition.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 33, No. 10, pp. 1921-1924, 2011.
  [pdf]

• Vidit Jain and Erik Learned-Miller.
  Online domain-adaptation of a pre-trained cascade of classifiers.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
  [pdf]

• David L. Smith, Jacqueline Feild, and Erik Learned-Miller.
  Enforcing similarity constraints with integer programming for better scene text recognition.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
  [pdf]

• Robert Walls, Brian N. Levine, and Erik Learned-Miller.
  Forensic triage for mobile phones with DEC0DE.
  USENIX Security Symposium, 2011.
  [pdf]

• Ralph E. Miller, Erik Learned-Miller, Peter Trainer, Angela Paisley and Volker Blanz.
  Early diagnosis of acromegaly: computers vs clinicians.
  Clinical Endocrinology, Vol. 75, No. 2, pp. 226-231, 2011.
  [pdf]

• Andrew Kae, David A. Smith and Erik Learned-Miller.
  Learning on the fly: a font-free approach towards multilingual OCR.
  International Journal on Document Analysis and Recognition (IJDAR), Vol. 14, No. 3, 13 pages, 2011.
  [pdf]

• Mastooreh (Negin) Salajegheh, Yue Wang, Kevin Fu, Anxiao (Andrew) Jiang, and Erik Learned-Miller.
  Exploiting Half-Wits: Smarter storage for low-power devices.
  9th USENIX Conference on File and Storage Technologies (FAST) , 2011.
  [pdf]

• John Tuttle, Robert J. Walls, Erik Learned-Miller, and Brian Neil Levine.
  Reverse engineering for mobile systems forensics with Ares.
  In Proceedings of the ACM: Workshop on Insider Threats, 2010.
  [pdf]

• Vidit Jain and Erik Learned-Miller.
  FDDB: A benchmark for face detection in unconstrained settings.
  UMass Amherst Technical Report UM-CS-2010-009, 11 pages, 2010.
  [pdf]

• Andrew Kae, Gary B. Huang, Carl Doersch, and Erik Learned-Miller.
  Improving state-of-the-art OCR through high-precision document-specific modeling.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
  [pdf]

• Gary B. Huang and Erik Learned-Miller.
  Learning class-specific image transformations with higher-order Boltzmann machines.
  In Workshop on Structured Models in Computer Vision at IEEE CVPR, 2010.
  [pdf]

• Andrew Kae, Gary B. Huang, and Erik Learned-Miller.
  Bounding the probability of error for high precision recognition.
  UMass Amherst Technical Report UM-CS-2009-031, 12 pages, 2009.
  [pdf]

• Andrew Kae and Erik Learned-Miller.
  Learning on the fly: Font free approaches to difficult OCR problems.
  Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2009.
  [pdf]

• Jerod Weinman, Erik Learned-Miller and Allen Hanson.
  Scene text recognition using similarity and a lexicon with sparse belief propagation.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Special Issue on Probabilistic Graphical Models, Vol. 31, No. 10, pp. 1733-1746, 2009.
  [pdf]

• Marwan Mattar, Michael G. Ross and Erik Learned-Miller.
  Non-parametric curve alignment.
  International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2009.
  [pdf]

• Erik Learned-Miller and Joseph DeStefano.
  A probabilistic upper bound on differential entropy.
  IEEE Transactions on Information Theory, Vol. 54, No. 11, pp. 5223-5230, 2008.
  [pdf]

• Andras Ferencz, Erik Learned-Miller, and Jitendra Malik.
  Learning to locate informative features for visual identification.
  International Journal of Computer Vision: Special Issue on Learning and Vision, Vol. 77, No. 1, pp. 3-24, May, 2008.
  [pdf]

• Tamara L. Berg, Alex C. Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Erik Learned-Miller, and David Forsyth.
  Names and faces.
  To appear International Journal of Computer Vision, 2008.
  

• Gary B. Huang, Michael J. Jones, and Erik Learned-Miller.
  LFW results using a combined Nowak plus MERL recognizer.
  In The Workshop on Faces in Real-Life Images at European Conference on Computer Vision, 2008.
  [pdf]

• Gary B. Huang, Marwan Mattar, Tamara Berg, and Erik Learned-Miller.
  Labeled faces in the wild: A database for studying face recognition in unconstrained environments.
  In The Workshop on Faces in Real-Life Images at European Conference on Computer Vision, 2008.
  [pdf]

• Jerod Weinman, Erik Learned-Miller, and Allen Hanson.
  A discriminative semi-Markov model for robust scene text recognition.
  In International Conference on Pattern Recognition (ICPR), 2008.
  [pdf]

• Erik Learned-Miller, Peter Trainer, Angela Paisley, Volker Blanz, and Ralph Miller.
  Acromegalic features: Recognition by physicians versus a computer model.
  In The Program and Abstracts Book of the 90th Meeting of the Endocrine Society, San Francisco, June, 2008.

• Gary B. Huang, Manjunath Narayana, and Erik Learned-Miller.
  Towards unconstrained face recognition.
  In The Sixth IEEE Computer Society Workshop on Perceptual Organization in Computer Vision IEEE CVPR, 2008.
  [pdf]

• Gary B. Huang, Vidit Jain, and Erik Learned-Miller.
  Unsupervised joint alignment of complex images.
  International Conference on Computer Vision (ICCV), 2007.
  [pdf]

• Vidit Jain, Erik Learned-Miller, and Andrew McCallum.
  People-LDA: Anchoring topics to people using face recognition.
  International Conference on Computer Vision (ICCV), 2007.
  [pdf]

• Manjunatha Jagalur, Chris Pal, Erik Learned-Miller, R. Thomas Zoeller and David Kulp.
  Analyzing in situ gene expression in the mouse brain with image registration, feature extraction and block clustering.
  BMC Bioinformatics, 8(Suppl 10):S5, 2007.
  [pdf]

• Gary B. Huang, Manu Ramesh, Tamara Berg and Erik Learned-Miller.
  Labeled Faces in the Wild: A database for studying face recognition in unconstrained environments.
  UMass Amherst Technical Report 07-49, 11 pages, 2007.
  [pdf]

• Michael Wick, Michael G. Ross and Erik Learned-Miller.
  Context-sensitive error correction: Using topic models to improve OCR.
  Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2007.
  [pdf]

• Jerod Weinman, Erik Learned-Miller, and Allen Hanson.
  Fast lexicon-based scene text recognition with sparse belief propagation.
  Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2007.
  [pdf]

• Gary C. Huang, Erik Learned-Miller, and Andrew McCallum.
  Cryptogram decoding for OCR using numerization strings.
  Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2007.
  [pdf]

• David Walker Duhon, Jerod Weinman and Erik Learned-Miller.
  Techniques and applications for persistent backgrounding in a humanoid torso robot.
  IEEE International Conference on Robotics and Automation (ICRA), 2007.
  [pdf]

• Marwan Mattar and Erik Learned-Miller.
  Improved generative models for continuous image features through tree-structured non-parametric distributions.
  UMass Amherst Technical Report 06-57, 10 pages, 2006.
  [pdf]

• Dov Katz, Emily Horrell, Yuandong Yang, Brendan Burns, Thomas Buckley, Anna Grishkan, Volodymyr Zhylkovskyy, Oliver Brock, and Erik Learned-Miller.
  The UMass mobile manipulator UMan: An experimental platform for autonomous mobile manipulation.
  In Workshop on Manipulation in Human Environments, at Robotics: Science and Systems, 2006.
  [pdf]

• Vidit Jain, Andras Ferencz and Erik Learned-Miller.
  Discriminative training of hyper-feature models for object identification.
  Proceedings of the British Machine Vision Conference (BMVC), Volume 1, pp. 357-366, 2006.
  [pdf]

• Erik Learned-Miller, Qifeng Lu, Angela Paisley, Peter Trainer, Volker Blanz, Katrin Dedden, and Ralph E. Miller.
  Detecting acromegaly: Screening for disease with a morphable model.
  Medical Image Computing and Computer-Assisted Intervention (MICCAI), Volume 2, pp. 495-503, 2006.
  [pdf]

• Jerod Weinman and Erik Learned-Miller.
  Improving recognition of novel input with similarity.
  In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Volume 1, pp. 308-315, 2006.
  [pdf]

• Ron Bekkerman, Mehran Sahami and Erik Learned-Miller.
  Combinatorial Markov random fields.
  Proceedings of the European Conference on Machine Learning (ECML) 17, pp. 30-41, 2006.
  [pdf]

• Erik Learned-Miller.
  Data driven image models through continuous joint alignment.
  In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 28:2, pp. 236-250, 2006.
  [pdf]

• Manjunatha N. Jagalur, Chris Pal, Erik Learned-Miller, R. T. Zoeller and David Kulp.
  The processing and analysis of in situ gene expression images of the mouse brain.
  Workshop on New Problems and Methods in Computational Biology, at Neural Information Processing Systems, 2006.
  [pdf]

• Jerod J. Weinman, Allen Hanson and Erik Learned-Miller.
  Joint feature selection for object detection and recognition.
  UMass Amherst Technical Report 06-54, 8 pages, 2006.
  [pdf]

• Gary Huang, Erik Learned-Miller and Andrew McCallum.
  Cryptogram decoding for optical character recognition.
  UMass Amherst Technical Report 06-45, 12 pages, 2006.
  [pdf]

• Andras Ferencz, Erik Learned-Miller, and Jitendra Malik.
  Building a classification cascade for visual identification from one example.
  In International Conference on Computer Vision (ICCV), pp. 286-293, 2005.
  [pdf]

• Andras Ferencz, Erik Learned-Miller and Jitendra Malik.
  Learning hyper-features for visual identification.
  In Neural Information Processing Systems (NIPS) 17, pp. 425-432, 2005.
  [pdf]

• Erik Learned-Miller and Parvez Ahammad.
  Joint MRI bias removal using entropy minimization across images.
  In Neural Information Processing Systems (NIPS) 17, pp. 761-768, 2005.
  [pdf]

• Lilla Zollei, Erik Learned-Miller, Eric Grimson, and William Wells.
  Efficient population registration of 3D data.
  In Workshop on Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends, at the International Conference of Computer Vision (best paper award), 2005.
  [pdf]

• Erik Learned-Miller and Vidit Jain.
  Many heads are better than one: Jointly removing bias from multiple MRs using nonparametric maximum likelihood.
  In Proceedings of Information Processing in Medical Imaging, pp. 615-626, 2005.
  [pdf]

• Dimitri A. Lisin, Marwan A. Mattar, Matthew B. Blaschko, Mark C. Benfield, and Erik G. Learned-Miller.
  Combining local and global features for object class recognition.
  In Workshop on Learning in Computer Vision and Pattern Recognition at IEEE CVPR, 2005.
  [pdf]

• Marwan A. Mattar, Allen R. Hanson, and Erik G. Learned-Miller.
  Sign classification using local and meta-features.
  In Proceedings of the IEEE Workshop on Computer Vision Applications for the Visually Impaired (in conjunction with CVPR), 2005.
  [pdf]

• Erik Learned-Miller, Qifeng Lu, Angela Paisley, Peter Trainer, Volker Blanz, Katrin Dedden, and Ralph Miller.
  Early diagnosis of acromegaly by facial pattern recognition.
  Abstract for The Ninth International Pituitary Congress, San Diego, CA, 2005.

• Joseph DeStefano, Qifeng Lu, and Erik Learned-Miller.
  A probabilistic upper bound on differential entropy.
  UMass Amherst Technical Report 05-12, 2005.
  [pdf]

• Marwan A. Mattar, Allen R. Hanson, and Erik G. Learned-Miller.
  Sign classification for the visually impaired.
  University of Massachusetts Technical Report 05-14, 2005.
  [pdf]

• Qifeng Lu, Erik Learned-Miller, Angela Paisley, Peter Trainer, Volker Blanz, Katrin Dedden, and Ralph Miller.
  Detecting acromegaly: Screening for diseases with a morphable model.
  UMass Amherst Technical Report 05-37, 2005.
  [pdf]

• Tamara Berg, Alex Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Erik Learned-Miller and David Forsyth.
  Names and faces in the news.
  In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, pp. 848-854, 2004.
  [pdf]

• Erik Learned-Miller.
  Hyperspacings and the estimation of information theoretic quantities.
  UMass Amherst Technical Report 04-104, 2004.
  [pdf]

• Erik Learned-Miller and John W. Fisher, III.
  ICA using spacings estimates of entropy.
  Journal of Machine Learning Research (JMLR), Volume 4, pp. 1271-1295, 2003.
  [pdf]

• Erik Miller and Christophe Chefd'hotel.
  Practical non-parametric density estimation on a transformation group for vision.
  In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Volume 2, pp. 114-121, 2003.
  [pdf]

• Kinh Tieu and Erik Miller.
  Unsupervised color constancy.
  In Neural Information Processing Systems (NIPS) 15, pp. 1303-1310, 2003.
  [pdf]

• Erik Miller.
  A new class of entropy estimators for multi-dimensional densities.
  International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2003.
  [pdf]

• Erik Miller and John W. Fisher, III.
  ICA using spacings estimates of entropy.
  Fourth International Symposium on Independent Components Analysis and Blind Signal Separation, 2003.
  [pdf]

• Erik Miller and John W. Fisher, III.
  Independent components analysis by direct entropy minimization.
  UC Berkeley Technical Report CSD-03-1221, 25 pages, 2003.
  [pdf]

• Chris Stauffer, Erik Miller and Kinh Tieu.
  Transform-invariant image decomposition with similarity templates.
  In Neural Information Processing Systems (NIPS) 14, pp. 1295-1302, 2002.
  [pdf]

• Erik Miller.
  Learning from one example in machine vision by sharing probability densities.
  Ph.D. Thesis, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2002.
  [pdf]

• Simon Warfield, Jan Rexilius, Petra Huppi, Terrie Inder, Erik Miller, William Wells, Gary Zientara, Ferenc Jolesz, and Ron Kikinis.
  A binary entropy measure to assess nonrigid registration algorithms.
  Proceedings of Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 266-274, 2001.
  [pdf]

• Erik Miller and Kinh Tieu.
  Color eigenflows: Statistical modeling of joint color changes.
  International Conference on Computer Vision (ICCV), Volume 1, pp. 607-614, 2001.
  [pdf]

• Erik Miller, Kinh Tieu and Eric Grimson.
  Lighting invariance through joint color change models.
  Proceedings of Workshop on Identifying Object Across Variations in Lighting: Psychophysics and Computation, at IEEE Conference on Computer Vision and Pattern Recognition, 2001.
  [pdf]

• Erik Miller, Kinh Tieu and Chris Stauffer.
  Learning object-independent modes of variation with feature flow fields.
  Massachusetts Institute of Technology, AI-Memo: AIM-2001-021, 9 pages, 2001.
  [pdf]

• Simon Warfield, Petra Huppi, Terrie Inder, Erik Miller, William Wells, Gary Zientara, Ferenc Jolesz, and Ron Kikinis.
  An intrinsic coordinate system of the developing human brain.
  Fifth International Conference on Cognitive and Neural Systems, Boston, MA, page 28, 2001.
  [pdf]

• Erik Miller, Nick Matsakis, and Paul Viola.
  Learning from one example through shared densities on transforms.
  Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Volume 1, pp. 464-471, 2000.
  [pdf]

• Erik Miller.
  Alternative tilings for improved surface area estimates by local counting algorithms.
  In Computer Vision and Image Understanding (CVIU), Volume 74, pages 193-211, 1999.
  [pdf]

• Erik Miller and Paul Viola.
  Ambiguity and constraint in mathematical expression recognition.
  Proceedings of the National Conference of Artificial Intelligence (AAAI), pp. 784-791, 1998.
  [pdf]

• Erik Miller.
  An analysis of surface area estimates of binary volumes under three tilings.
  Masters Thesis, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1997.
  [pdf]