140 Governors Drive, Office 248
Amherst, MA 01003

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

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

• Marwan Mattar, Michael G. Ross and Erik Learned-Miller.
  Non-parametric curve alignment.
  International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2009.
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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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• 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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• 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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• 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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• 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.
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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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• 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.
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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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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• Gary Huang, Erik Learned-Miller and Andrew McCallum.
  Cryptogram decoding for optical character recognition.
  UMass Amherst Technical Report 06-45, 12 pages, 2006.
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• 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.
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• 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.
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