140 Governors Drive, Office 200
Amherst, MA 01003

E-mail: elm at cs.umass.edu
Computer Vision Lab

News:

• We have started work on a New Building! Check out the College web pages for architectural drawings and renderings.
• On September 1, 2022, I started my new position as Chair of the Faculty in the Manning College of Information and Computer Sciences. I continue to co-direct the Computer Vision Lab with Subhransu Maji.

• 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]

• 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]

• 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]

• Pia Bideau, Erik Learned-Miller, Cordelia Schmid, and Karteek Alahari.
  The right spin: Learning object motion from rotation-compensated flow fields.
  International Journal of Computer Vision, August, 2023.
  [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.
  

• 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]

• 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]

• 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]

• 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 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]

• 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]

• Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sebastien M. R. Arnold, Ese Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, David Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha.
  A domain-agnostic approach for characterization of lifelong learning systems.
  Neural Networks, Volume 160, 70 pages, 2023.
  [ScienceDirect]

• Zitian Chen, Yikang Shen, Mingyu Ding, Zhenfang Chen, Hengshuang Zhao, Erik Learned-Miller and Chuang Gan.
  Mod-Squad: Designing mixtures of experts as modular multi-task learners.
  Computer Vision and Pattern Recognition (CVPR), 10 pages, 2023.
  [pdf]

• Ashish Singh, Michael Jones, and Erik Learned-Miller.
  EVAL: Explainable Video Anomaly Localization.
  Computer Vision and Pattern Recognition (CVPR), 10 pages, 2023.
  [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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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 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]

• 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]

• Fabien Delattre, David Dirnfeld, Phat Nguyen, Stephen Scarano, Michael J. Jones, Pedro Miraldo, Erik Learned-Miller.
  Robust frame-to-frame camera rotation estimation in crowded scenes.
  International Conference on Computer Vision (ICCV), 10 pages, 2023.
  [pdf] [Project page]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• Huaizu Jiang and Erik Learned-Miller.
  DCVNet: Dilated cost volume networks for fast optical flow.
  Winter Conference on Applications of Computer Vision (WACV), 8 pages, 2023.
  [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]

• Gopal Sharma, Bidya Dash, Aruni Roy Chowdhury, Matheus Gadelha, Marios Loizou, Liangliang Cao, Rui Wang, Erik Learned-Miller, Subhrnasu Maji, and Evangelos Kalogerakis.
  PriFit: Learning to fit primitives improves few shot point cloud segmentation.
  Eurographics Symposium on Geometry Processing (SGP), Volume 41, Number 5, 12 pages, 2022.
  [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]

• 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]

• Bruce Levin and Erik Learned-Miller.
  Log-concavity of multinomial likelihood functions under interval censoring constraints on frequencies or their partial sums.
  Statistics and Probability Letters, 6 pages, 2024.
  [arXiv][Paper]

• Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik Learned-Miller, Emma Brunskill, and Philip S. Thomas.
  Universal off-policy evaluation.
  In Neural Information Processing Systems (NeurIPS) , 2021.
  [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]

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

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

• 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][erratum]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• Ashutosh Singh, Ashish Singh, Aria Masoomi, Tales Imbriba, Erik Learned-Miller, and Deniz Erdogmus.
  Inv-Senet: Invariant self expression network for clustering under biased data.
  International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023.
  [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 Miller.
  A new class of entropy estimators for multi-dimensional densities.
  International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2003.
  [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 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]

• 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]

• 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]

• 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]

• Shifan Zhu, Zhipeng Tang, Michael Yang, Erik Learned-Miller and Donghyun Kim.
  Event Camera-based Visual Odometry for Dynamic Motion Tracking of a Legged Robot Using Adaptive Time Surface.
  IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023.
  [to appear]

• 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, 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• Ke Xiao, Erik Learned-Miller, Evangelos Kalogerakis, James Priest, Madalina Fiterau.
  Machine Learning for Automated Mitral Regurgitation Detection from Cardiac Imaging.
  Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023.
  [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]

• 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 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]

• 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]

• 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]

• 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]

• 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]

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

• 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]

• 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]

• 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]

• 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]

• 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, 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]

• 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]

• 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]

• 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]

• 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 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]

• 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]

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

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

• 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]

• 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]

• 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]

• 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]

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

• 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]