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.

• 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]

• 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]

• 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]

• 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]

• 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]

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

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

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

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

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

• 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]

• 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]

• 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]

• 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]

• 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]

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

• 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]

• 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]

• 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.
  Improvements in joint domain-range modeling for background subtraction.
  Proceedings of the British Machine Vision Conference (BMVC), 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]

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

• 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]

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

• 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]

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

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

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

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

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

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

• 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]

• 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]

• 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]

• 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]

• 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]

• 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, 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]

• 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]

• 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, 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.

• 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]

• 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]

• 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]

• 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]

• 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]

• 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, 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]

• 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]

• 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 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 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]

• 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]

• 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]

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

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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 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]

• 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 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.
  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]

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

• 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]

• 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.

• 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, 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]

• 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]

• 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, Vol. 77, No. 1, pp. 3-24, May, 2008.
  [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]

• 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]

• 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]

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

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

• 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, 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]

• 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, 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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 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]

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

• 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]

• 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, 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]

• 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]

• 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]

• 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]

• 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]

• 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]

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