140 Governors Drive, Office 248
Amherst, MA 01003

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

• 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]

• 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.
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• Gary B. Huang, Andrew Kae, Carl Doersch, and Erik Learned-Miller.
  Bounding the Probability of Error for High Precision Optical Character Recognition
  To appear in Journal of Machine Learning Research (JMLR), 2012.
  [pdfSoon]

• 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.
  To appear in ACM Transactions on Embedded Computing Systems. Special Issue on Probabilistic Embedded Computing , 25 pages, 2012.
  [pdf_coming_soon]

• 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), 13 pages, 2011.
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• Ralph E. Miller, Erik Learned-Miller, Peter Trainer, Angela Paisley and Volker Blanz.
  Early diagnosis of acromegaly: computers vs clinicians.
  Clinical Endocrinology, Volume 75, pages 226-231, 2011.
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• 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.
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• 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.
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• 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, Volume 77, Number 1, pp. 3-24, May, 2008.
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• 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 Learned-Miller and Joseph DeStefano.
  A probabilistic upper bound on differential entropy.
  IEEE Transactions on Information Theory, Volume 54, Number 11, pp. 5223-5230, 2008.
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• 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.
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• 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.
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• 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.
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• 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.
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• Douglas Cohen, Jonathan Lustgarten, Erik Miller, Alexander Khandji and Robert Goodman.
  Effects of coregistration of MR to CT images on MR stereotactic accuracy.
  Journal of Neurosurgery, Volume 82, pp. 772-779, 1995.

• Robert Malison, Erik Miller, Robin Greene, Greg McCarthy, Dennis Charney and Robert Innis.
  Computer assisted coregistration of multislice SPECT and MR brain images by fixed external fiducials.
  Journal of Computer Assisted Tomography, Volume 17, pp. 952-960, 1993.

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

• 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]

• 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]

• Marwan Mattar, Michael G. Ross and Erik Learned-Miller.
  Non-parametric curve alignment.
  International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2009.
  [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.
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• Gary B. Huang, Vidit Jain, and Erik Learned-Miller.
  Unsupervised joint alignment of complex images.
  International Conference on Computer Vision (ICCV), 2007.
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• Vidit Jain, Erik Learned-Miller, and Andrew McCallum.
  People-LDA: Anchoring topics to people using face recognition.
  International Conference on Computer Vision (ICCV), 2007.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• Kinh Tieu and Erik Miller.
  Unsupervised color constancy.
  In Neural Information Processing Systems (NIPS) 15, pp. 1303-1310, 2003.
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• Erik Miller.
  A new class of entropy estimators for multi-dimensional densities.
  International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2003.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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]

• 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.
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• 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]

• 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.
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• 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.
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• 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.
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• 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]

• 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.

• 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.

• 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.
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• Laura Sevilla Lara and Erik Learned-Miller.
  Distribution fields.
  UMass Amherst Technical Report UM-CS-2011-027, 18 pages, 2011.
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• 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.
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• Erik Miller and John W. Fisher, III.
  Independent components analysis by direct entropy minimization.
  UC Berkeley Technical Report CSD-03-1221, 25 pages, 2003.
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• 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]