Erik Learned-Miller Erik G. Learned-Miller
Assistant Professor of Computer Science
University of Massachusetts, Amherst

140 Governors Drive, Office 248
Amherst, MA 01003

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

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Bioinformatics | Computer Vision | Information Theory | Machine Learning | Medicine and Medical Imaging | Robotics | Statistics
Bioinformatics
Computer Vision
  • 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]

  • 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]

  • 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, 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]

  • 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]

  • 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]

  • 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.
    [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, 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]

  • 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]

  • 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]

  • 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]

  • 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]

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

  • 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]

  • 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]

  • 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]

  • 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]

  • 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]

  • 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 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, 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]

Information Theory
Machine Learning
  • 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]

  • 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.
    [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]

  • 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]

  • 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]

  • 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]

  • 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]

  • 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]

  • 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]

  • 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 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.
    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]

  • 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, 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]

  • 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]

Medicine and Medical Imaging
Robotics
Statistics