This includes any coporate activity within the last 5 years involving more than $5000 where I have a personal or professional interest or any role in which I have corporate responsibility.
Michael J. Black received his B.Sc. from the University of British Columbia (1985), his M.S. from Stanford (1989), and his Ph.D. in computer science from Yale University (1992). After research at NASA Ames and post-doctoral research at the University of Toronto, he joined the Xerox Palo Alto Research Center in 1993 where he later managed the Image Understanding Area and founded the Digital Video Analysis group. From 2000 to 2010 he was on the faculty of Brown University in the Department of Computer Science (Assoc. Prof. 2000-2004, Prof. 2004-2010). He is a founding director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he leads the Perceiving Systems department. He is also a Distinguished Amazon Scholar, an Honorarprofessor at the University of Tuebingen, and Adjunct Professor at Brown University.
Black is a foreign member of the Royal Swedish Academy of Sciences. He is a recipient of the 2010 Koenderink Prize for Fundamental Contributions in Computer Vision and the 2013 Helmholtz Prize for work that has stood the test of time. His work has won several paper awards including the IEEE Computer Society Outstanding Paper Award (CVPR'91). His work received Honorable Mention for the Marr Prize in 1999 and 2005. His early work on optical flow has been widely used in Hollywood films including for the Academy-Award-winning effects in “What Dreams May Come” and “The Matrix Reloaded.” He has contributed to several influential datasets including the Middlebury Flow dataset, HumanEva, and the Sintel dataset. Black has coauthored over 200 peer-reviewed scientific publications.
He is also active in commercializing scientific results, is an inventor on 10 issued patents, and has advised multiple startups. He uniquely combines computer vision, graphics, and machine learning to solve problems in the clothing industry. In 2013, he co-founded Body Labs Inc., which used computer vision, machine learning, and graphics technology licensed from his lab to commercialize "the body as a digital platform." Body Labs was acquired by Amazon in 2017.
Black's research interests in machine vision include optical flow estimation, 3D shape models, human shape and motion analysis, robust statistical methods, and probabilistic models of the visual world. In computational neuroscience his work focuses on probabilistic models of the neural code and applications of neural decoding in neural prosthetics.
Michael Black received his B.Sc. from the University of British Columbia (1985), his M.S. from Stanford (1989), and his Ph.D. from Yale University (1992). After post-doctoral research at the University of Toronto, he worked at Xerox PARC as a member of research staff and area manager. From 2000 to 2010 he was on the faculty of Brown University in the Department of Computer Science (Assoc. Prof. 2000-2004, Prof. 2004-2010). He is one of the founding directors at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he leads the Perceiving Systems department. He is also a Distinguished Amazon Scholar, an Honorarprofessor at the University of Tuebingen, and Adjunct Professor at Brown University. His work has won several awards including the IEEE Computer Society Outstanding Paper Award (1991), Honorable Mention for the Marr Prize (1999 and 2005), the 2010 Koenderink Prize for Fundamental Contributions in Computer Vision, and the 2013 Helmholtz Prize for work that has stood the test of time. He is a foreign member of the Royal Swedish Academy of Sciences. In 2013 he co-founded Body Labs Inc., which was acquired by Amazon in 2017.
Michael Black received his B.Sc. from the University of British Columbia (1985), his M.S. from Stanford (1989), and his Ph.D. from Yale University (1992). He has held positions at the University of Toronto, Xerox PARC, and Brown Unviversity. He is one of the founding directors at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he leads the Perceiving Systems department. He is a Distinguished Amazon Scholar, an Honorarprofessor at the University of Tuebingen, and Adjunct Professor at Brown University. His work has won several awards including the IEEE Computer Society Outstanding Paper Award (1991), Honorable Mention for the Marr Prize (1999 and 2005), the 2010 Koenderink Prize, and the 2013 Helmholtz Prize. He is a foreign member of the Royal Swedish Academy of Sciences. In 2013 he co-founded Body Labs Inc., which was acquired by Amazon in 2017.
Yale University, New Haven, CT
Ph.D., Computer Science, 1992.
Stanford University, Stanford, CA
M.S., Computer Science, 1989.
The University of British Columbia, Vancouver, BC
B.Sc., Honours Computer Science, 1985.
Alumni Research Award
University of British Columbia, Department of Computer Science, 2018.
Royal Swedish Academy of Sciences
Foreign member, Class for Engineering Sciences, since June 2015.
2013 Helmholtz Prize
for the paper: Black, M. J., and Anandan, P., "A framework for the robust estimation of optical flow,'' IEEE International Conference on Computer Vision, ICCV, pages 231-236, Berlin, Germany. May 1993.
2010 Koenderink Prize for Fundamental Contributions in Computer Vision,
with Sidenbladh, H. and Fleet, D. J. for the paper "Stochastic tracking of 3D human figures using 2D image motion,'' European Conference on Computer Vision, 2000.
Best Paper Award, Eurographics 2017, for the paper "Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs", by von Marcard, T., Rosenhahn, B., Black, M. J., Pons-Moll, G.
"Dataset Award" at the Eurographics Symposium on Geometry Processing 2016, with F. Bogo, J. Romero, and M. Loper, for the paper "FAUST: Dataset and evaluation for 3D mesh registration," CVPR 2014.
Best Paper Award, International Conference on 3D Vision (3DV), 2015, with A. O. Ulusoy and A. Geiger, for the paper "Towards Probabilistic Volumetric Reconstruction using Ray Potentials."
Best Paper Award, INI-Graphics Net, 2008, First Prize Winner of Category Research,
with S. Roth for the paper "Steerable random fields."
Best Paper Award, Fourth International Conference on Articulated Motion and Deformable Objects (AMDO-e 2006), with L. Sigal for the paper "Predicting 3D people from 2D pictures.''
Marr Prize, Honorable Mention, Int. Conf. on Computer Vision, ICCV-2005, Beijing, China, Oct. 2005 with S. Roth for the paper "On the spatial statistics of optical flow.''
Marr Prize, Honorable Mention, Int. Conf. on Computer Vision, ICCV-99, Corfu, Greece, Sept. 1999 with D. J. Fleet for the paper "Probabilistic detection and tracking of motion discontinuities.''
IEEE Computer Society, Outstanding Paper Award, Conference on Computer Vision and Pattern Recognition, Maui, Hawaii, June 1991 with P. Anandan for the paper "Robust dynamic motion estimation over time.''
Commendation and Chief's Award, Henrico County Division of Police,
County of Henrico, Virginia, April 19, 2007.
University of Maryland, Invention of the Year, 1995, "Tracking and Recognizing Facial Expressions,'' with Y. Yacoob.
University of Toronto, Computer Science Students' Union Teaching Award for 1992-1993.
Max Planck Institute for Intelligent Systems
Tübingen, Germany
Director, 1/11 - present
Managing Director, 2/13 - 6/15, 3/18 - 11/18
Amazon
Tübingen, Germany
Distinguished Amazon Scholar, 11/17 - present
Eberhard Karls Universität Tübingen, Faculty of Science, Department of Computer Science
Tübingen, Germany
Honorarprofessor, 05/22/12 - present
Body Labs Inc.
New York, NY, USA
Co-founder, Science Advisor, Member of the Board, 01/13 - 10/2017
ETH Zürich, Dept. of Information Technology and Electrical Engineering
Zürich, Switzerland
Visting Professor, 04/2014 - 04/2016
Stanford University, Electrical Engineering
Stanford, CA
Visiting Professor, 5/11-4/12, 7/12-7/13
Brown University, Department of Computer Science,
Providence, RI
Adjunct Professor (Research), 1/11-present
Professor, 7/04-12/10
Associate Professor, 7/00-6/04
My research addressed the problem of estimating and explaining motion in image sequences. I developed methods detecting and tracking 2D and 3D human motion including the introduction of particle filtering for 3D human tracking and belief propagation for 3D human pose estimation. I worked on probabilistic models of images include the high-order Field of Experts model. I worked on 3D human shape estimation from images and video and developed applications of this technology. I also developed mathematical models for decoding neural signals. This included the first uses of particle filtering and Kalman filtering for decoding motor cortical neural activity and the first point-and-click cortical neural brain-machine-interface for people with paralysis.
Xerox Palo Alto Research Center,
Palo Alto, CA
Area Manager, Image Understanding Area, 1/96-7/00
Member of Research Staff, 9/93-12/95
Research included modeling image changes (motion, illumination, specularity, occlusion, etc.) in video as a mixture of causes. I developed methods of motion explanation; that is, the extraction of mid-level or high-level concepts from motion. This included the modeling and recognition of motion "features" (occlusion boundaries, moving bars, etc.), human facial expressions and gestures, and motion "texture" (plants, fire, water, etc.). I applied these methods to problems in video indexing, motion for video annotation, teleconferencing, and gestural user interfaces. Other research included robust learning of image-based models, regularization with transparency, anisotropic diffusion, and the recovery of multiple shapes from transparent textures.
University of Toronto,
Toronto, Ontario
Assistant Professor, Department of Computer Science, (8/92 - 9/93).
Research included the application of mixture models to optical flow, detection and tracking of surface discontinuities using motion information, and robust surface recovery in dynamic environments.
Yale University, (9/89-8/92)
New Haven, CT
Research Assistant, Department of Computer Science.
Research in the recovery of optical flow, incremental estimation, temporal continuity, applications of robust statistics to optical flow, the relationship between robust statistics and line processes, the early detection of motion discontinuities, and the role of representation in computer vision.
NASA Ames Research Center, (6/90-8/92)
Moffett Field, CA
Visiting Researcher, Aerospace Human Factors Research Division.
Developed motion estimation algorithms in the context of an autonomous Mars landing and nap-of-the-earth helicopter flight and studied the psychophysical implications of a temporal continuity assumption.
Advanced Decision Systems, (12/86-6/89)
Mountain View, CA
Computer Scientist, Image Understanding Group.
Research on spatial reasoning for robotic vehicle route planning and terrain analysis. Vision research including perceptual grouping, object-based translational motion processing, the integration of vision and control for an autonomous vehicle, object modeling using generalized cylinders, and the development of an object-oriented vision environment.
GTE Government Systems, (6/85-12/86)
Mountain View, CA
Engineer, Artificial Intelligence Group.
Developed expert systems for multi-source data fusion and fault location.
Miscellaneous, ('78-'85)
Summer undergraduate researcher at UBC; park ranger's assistant; volunteer firefighter, busboy; and probably my worst job: cleaning dog kennels.
I am interested in motion. What does motion tell us about the structure of the world and how can we compute this from video? How do humans and animals move? How does the brain control complex movement? My work combines computer vision, graphics and neuroscience to develop new models and algorithms to capture and analyze the motion of the world.
My Computer Vision research addresses:
My Graphics research addresses:
My Computational Neuroscience research addresses:
I also work on industrial applications in Fashion Science:
What is maybe unique about my work is the combination of the these themes. For example I study human motion from the inside (decoding neural activity in paralyzed humans) and the outside (with novel motion capture techniques).
Omid Taheri, MPI for Intelligent Systems, Tübingen
Qianli Ma, Int. Max Planck Research School, Intelligent Systems, Tübingen
Vassilis Choutas, MPI-ETH Center for Learning Systems, Co-supervised with Luc van Gool
Ahmed Osman, Int. Max Planck Research School, Intelligent Systems, Tübingen
Mohamed Hassan, MPI for Intelligent Systems, Tübingen
Partha Ghosh, Int. Max Planck Research School, Intelligent Systems, Tübingen
Soubhik Sanyal, Int. Max Planck Research School, Intelligent Systems, Tübingen
Daniel Cudeiro, MPI-ETH Center for Learning Systems, Co-supervised with Luc van Gool
Nadine Rüegg, MPI-ETH Center for Learning Systems, Co-supervised with Konrad Schindler
Eric Price, MPI for Intelligent Systems, Tübingen
Yinghao Huang, MPI for Intelligent Systems, Tübingen
Anurag Ranjan, MPI for Intelligent Systems, Tübingen
Jonas Wulff, Postdoctoral Researcher, MIT CSAIL
Thesis: Model-based Optical Flow: Layers, Learning, and Geometry, University of Tübingen, April 2018
Matthew Loper, Amazon,
Thesis: Human Shape Estimation using Statistical Body Models, University of Tübingen, May 2017
Silvia Zuffi, Research Scientist, IMATI-CNR, Institute for Applied Mathematics and Information Technologies, Milan Italy
Thesis: Shape Models of the Human Body for Distributed Inference, Brown University, May 2015
Aggeliki Tsoli, Post doctoral researcher, FORTH Institute, Crete,
Thesis: Modeling the Human body in 3D: Data Registration and Human Shape Representation, Department of Computer Science, Brown University, May 2014
Oren Freifeld, Assistant Professor, Dept. of Computer Science, Ben-Gurion Univ., Israel,
Thesis: Statistics on Manifolds with Applications to Modeling Shape Deformations, Division of Applied Mathematics, Brown University, August 2013
Peng Guan, Senior Software Engineer, Google,
Thesis: Virtual Human Bodies with Clothing and Hair: From Images to Animation, Department of Computer Science, Brown University, December 2012
Deqing Sun, Senior Research Scientist, NVIDIA Research,
Thesis: From Pixels to Layers: Joint Motion Estimation and Segmentation, Department of Computer Science, Brown University, July 2012
Alexandru Balan, Xbox Incubation Researcher, Microsoft
Thesis: Detailed Human Shape and Pose from Images, Department of Computer Science, Brown University, May 2010
Leonid Sigal, Associate Professor of Computer Science, Univ. of British Columbia (UBC)
Thesis: Continuous-state graphical models for object localization, pose estimation and tracking Department of Computer Science, Brown University, May 2008
Stefan Roth, Professor, Dept. of Computer Science, TU Darmstadt
Thesis: High-order Markov random fields for low-level vision. Dept. of Computer Science, Brown University,
May 2007 Winner of the Joukowsky Family Foundation Outstanding Dissertation Award
Frank Wood, Associate Professor of Computer Science, Univ. of British Columbia (UBC)
Thesis: Nonparametric Bayesian modeling of neural data. Department of Computer Science, Brown University
Hulya Yalcin, Assistant Professor, Department of Electronics and Communications Engineering, Istanbul Technical University, Turkey
Thesis: Implicit models of moving and static surfaces, Division of Engineering, Brown University, May 2004
Wei Wu, Associate Professor, Dept. of Statistics, Florida State
Thesis: Statistical models of neural coding in motor cortex, Division of Applied Math, Brown University. Co-supervised with David Mumford. May 2004.
Fernando De la Torre, Research Associate Professor, CMU and Facebook,
Thesis: Robust subspace learning for computer vision, La Salle School of Engineering. Universitat Ramon Llull, Barcelona, Spain. Jan. 2002
Hedvig Kjellstrom (nee Sidenbladh), Professor of Comptuer Science, KTH, Sweden
Thesis: Probabilistic Tracking and Reconstruction of 3D Human Motion in Monocular Video Sequences. Dept. of Numerical Analysis and Computer Science, KTH, Stockholm, Sweden 2001
Shanon Ju
Thesis: Estimating image motion in layers: The Skin and Bones model. University of Toronto. Jan. 1999
I belive that computer vision is advanced by careful evaluation and comparison. Consequently I have been involved in building several public datasets and evaluation websites.
3D human scans of multiple people in multiple poses with accurate ground truth correspondences: FAUST site.
Optical flow benchmark based on the animate film Sintel: MPI-Sintel site.
Annotated videos for action recognition: JHMDB site.
Image sequences, ground truth flow, and evaluation are all available on the Middlebury Flow site.
Multi-camera imagery with ground truth 3D human pose: HumanEva site.
Multi-camera imagery with ground truth 3D human pose. This predates HumanEva and the imagery is grayscale only. There is also software for partical filter tracking on the site.
My old Brown site has several image sequences used in my older publications. These include some classic sequences such as Yosemite, the Pepsi can, the SRI tree sequence, and the Flower Garden sequence.
This code is descrbed in
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles behind Them
Sun, D., Roth, S., and Black, M.J.
International Journal of Computer Vision (IJCV), 106(2):115-137, 2014.
(pdf)
Secrets of optical flow estimation and their principles
Sun, D., Roth, S., and Black, M. J.,
IEEE Conf. on Computer Vision and Pattern Recog., CVPR, June 2010.
(pdf)
This method implements many of the currently best known techniques for accurate optical flow and was once ranked #1 on the Middlebury evaluation (June 2010).
The software is made available for research pupropses. Please read the copyright statement and contact me for commerical licensing.
Matlab implementation of Black and Anandan robust dense optical flow algorithm
The method in 1 above is more accurate and also implements Black and Anandan plus much more.
The optical flow software here has been used by a number of graphics companies to make special effects for movies. This software is provided for research purposes only; any sale or use for commercial purposes is strictly prohibited.
Contact me for the password to download the software, stating that it is for research purposes.
Please contact me if you wish to use this code for commercial purpose.
If you are a commercial enterprise and would like assistance in using optical flow in your application, please contact me at my consulting address black@opticalflow.com.
This is EXPERIMENTAL software. It is provided to illustrate some ideas in the robust estimation of optical flow. Use at your own risk. No warranty is implied by this distribution.
There are two versions available. First, the original C code implementing the robust flow methods described in Black and Anandan '96:
Area-based optical flow: robust affine regression.
Dense optical flow: robust regularization.
Reference:
The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields,
Black, M. J. and Anandan, P.,
Computer Vision and Image Understanding, CVIU, 63(1), pp. 75-104, Jan. 1996.
(pdf), (pdf from publisher)
Software is from the ICCV'2001 paper with Fernando De la Torre.
De la Torre, F. and Black, M. J., Robust principal component analysis for computer vision, to appear: Int. Conf. on Computer Vision, ICCV-2001, Vancouver, BC. (postscript, 1.0MB)(pdf, 0.36MB), (abstract)
The code below provides a simple Matlab implementation of the Bayesian 3D person tracking system described in ECCV'00 and ICCV'01. It is too slow to be used to track the entire body but can be used to track various limbs and provides a basis for people who want to understand the methods better and extend them.
Learning image statistics for Bayesian tracking,
Sidenbladh, H. and Black, M. J.,
Int. Conf. on Computer Vision, ICCV-2001, Vancouver, BC, Vol. II, pp. 709-716.
(postscript, 2.8MB)(pdf, 0.38MB), (abstract)
Stochastic tracking of 3D human figures using 2D image motion,
Sidenbladh, H., Black, M. J., and Fleet, D.J.,
European Conference on Computer Vision, D. Vernon (Ed.), Springer Verlag, LNCS 1843, Dublin, Ireland, pp. 702-718 June 2000.
(postscript)(pdf), (abstract)
Software. (Note: if you uncompress and untar this on a PC using Winzip, the path names may be lost which will cause Matlab to fail when you load the .mat files. Instead uncompress/untar using gunzip and tar.)
Brown Institute for Brain Science (BIBS), Member
How to reach me:
Mailing address
Michael J. Black
Max Planck Institute for Intelligent Systems
Spemannstrasse 41
72076 Tübingen
Germany
For more information including our address and directions, see the department CONTACT page.
I receive more email than I can read, let alone respond to. I apologize if you do not get a response. If you do not hear from me, consider the following:
My assistant reads mail sent to me at black@is.mpg.de. If you have something particularly private, you can email me at black@cs.brown.edu and only I will read it.
Estimating Human Motion: Past, Present, and Future. (with full bibliography)
40 Years DAGM - Invited Talks, GCPR 2018 (pdf)
What is optical flow for? On prediction, persistence and structure.
Workshop on What is Optical Flow For? ECCV, Munich, Sept. 2018.
(ppt 76MB)
The Future and Generative Models: A Case Study of Human Bodies in Motion.
2-hour course given at the Int. Computer Vision Summer School, July 2016.
(ppt 1.5GB)
On building digital humans.
An overview of our work on 3D body shape, based on a series of talks during 2015.
(ppt 1GB)
Sun, D., Roth, S., Black, M. J.
(CS-10-03), Brown University, Department of Computer Science, January 2013 (techreport)
Sekunova, A., Black, M., Parkinson, L., Barton, J. J. S.
Perception, 42(2):176-186, 2013 (article)
Hirshberg, D., Loper, M., Rachlin, E., Black, M.
In European Conf. on Computer Vision (ECCV), pages: 242-255, LNCS 7577, Part IV, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)
Wulff, J., Butler, D. J., Stanley, G. B., Black, M. J.
In ECCV Workshop on Unsolved Problems in Optical Flow and Stereo Estimation, pages: 168-177, Part II, LNCS 7584, (Editors: A. Fusiello et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)
Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J.
In European Conf. on Computer Vision (ECCV), pages: 611-625, Part IV, LNCS 7577, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)
Foster, J. D., Nuyujukian, P., Freifeld, O., Ryu, S., Black, M. J., Shenoy, K. V.
In 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’12), pages: 2736 -2739 , IEEE, San Diego, August 2012 (inproceedings)
Guan, P., Reiss, L., Hirshberg, D., Weiss, A., Black, M. J.
ACM Trans. on Graphics (Proc. SIGGRAPH), 31(4):35:1-35:10, July 2012 (article)
Zuffi, S., Freifeld, O., Black, M. J.
In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3546-3553, IEEE, June 2012 (inproceedings)
Stanley, G., Jin, J., Wang, Y., Desbordes, G., Wang, Q., Black, M., Alonso, J.
Journal of Neuroscience, 32(26):9073-9088, June 2012 (article)
Ghosh, S., Sudderth, E., Loper, M., Black, M.
In Advances in Neural Information Processing Systems 25 (NIPS), pages: 2006-2014, (Editors: P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger), MIT Press, 2012 (inproceedings)
Weiss, A., Hirshberg, D., Black, M. J.
In Consumer Depth Cameras for Computer Vision: Research Topics and Applications, pages: 99-118, 6, (Editors: Andrea Fossati and Juergen Gall and Helmut Grabner and Xiaofeng Ren and Kurt Konolige), Springer-Verlag, 2012 (incollection)
Sun, D., Sudderth, E., Black, M. J.
In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 1768-1775, IEEE, 2012 (inproceedings)
Hauberg, S., Freifeld, O., Black, M. J.
In Advances in Neural Information Processing Systems (NIPS) 25, pages: 2033-2041, (Editors: P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger), MIT Press, 2012 (inproceedings)
Weiss, A., Hirshberg, D., Black, M.
In Int. Conf. on Computer Vision (ICCV), pages: 1951-1958, IEEE, Barcelona, November 2011 (inproceedings)
Hirshberg, D. A., Loper, M., Rachlin, E., Tsoli, A., Weiss, A., Corner, B., Black, M. J.
In 2nd International Conference on 3D Body Scanning Technologies, pages: 76-86, (Editors: D’Apuzzo, Nicola), Hometrica Consulting, Lugano, Switzerland, October 2011 (inproceedings)
Hochberg, L., Simeral, J., Black, M., Bacher, D., Barefoot, L., Berhanu, E., Borton, D., Cash, S., Feldman, J., Gallivan, E., Homer, M., Jarosiewicz, B., King, B., Liu, J., Malik, W., Masse, N., Perge, J., Rosler, D., Schmansky, N., Travers, B., Truccolo, W., Nurmikko, A., Donoghue, J.
33rd Annual International IEEE EMBS Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, August 2011 (conference)
Sigal, L., Isard, M., Haussecker, H., Black, M. J.
International Journal of Computer Vision, 98(1):15-48, Springer Netherlands, May 2011 (article)
Kim, S., Simeral, J. D., Hochberg, L. R., Donoghue, J. P., Friehs, G. M., Black, M. J.
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(2):193-203, April 2011 (article)
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., Szeliski, R.
International Journal of Computer Vision, 92(1):1-31, March 2011 (article)
Foster, J., Freifeld, O., Nuyujukian, P., Ryu, S., Black, M. J., Shenoy, K.
In Proc. 5th Int. IEEE EMBS Conf. on Neural Engineering, pages: 613-616, IEEE, 2011 (inproceedings)
(J. Neural Engineering Highlights of 2011 Collection. JNE top 10 cited papers of 2010-2011.)
Simeral, J. D., Kim, S., Black, M. J., Donoghue, J. P., Hochberg, L. R.
J. of Neural Engineering, 8(2):025027, 2011 (article)
Sekunova, A., Black, M. J., Parkinson, L., Barton, J. S.
Vision Sciences Society, 2011 (conference)
Andriluka, M., Sigal, L., Black, M. J.
In Visual Analysis of Humans: Looking at People, pages: 253-274, (Editors: Moesland and Hilton and Kr"uger and Sigal), Springer-Verlag, London, 2011 (incollection)
Roth, S., Black, M. J.
In Markov Random Fields for Vision and Image Processing, pages: 297-310, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)
Franquemont, L., Vargas-Irwin, C., Black, M., Donoghue, J.
2011 Abstract Viewer and Itinerary Planner, Online, Society for Neuroscience, 2011, Online (conference)
Foster, J., Freifeld, O., Nuyujukian, P., Ryu, S., Black, M., Shenoy, K.
2011 Abstract Viewer and Itinerary Planner, Society for Neuroscience, 2011, Online (conference)
Roth, S., Black, M. J.
In Markov Random Fields for Vision and Image Processing, pages: 377-387, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)
Kelly, S., Stanley, G., Jin, J., Wang, Y., Desbordes, G., Wang, Q., Black, M., Alonso, J.
2011 Abstract Viewer and Itinerary Planner, Society for Neuroscience, 2011, Online (conference)
Hochberg, L., Bacher, D., Barefoot, L., Berhanu, E., Black, M., Cash, S., Feldman, J., Gallivan, E., Homer, M., Jarosiewicz, B., King, B., Liu, J., Malik, W., Masse, N., Berge, J., Rosler, D., Schmansky, N., Simeral, J., Travers, B., Truccolo, W., Donoghue, J.
2011 Abstract Viewer and Itinerary Planner, Society for Neuroscience, 2011, Onine (conference)
Vargas-Irwin, C., Franquemont, L., Black, M., Donoghue, J.
Neural Control of Movement, 21st Annual Conference, 2011 (conference)
Guan, P., Freifeld, O., Black, M. J.
In European Conf. on Computer Vision, (ECCV), pages: 285-298, Springer-Verlag, September 2010 (inproceedings)
(Featured in Nature’s Research Highlights (Nature, Vol 466, 29 July 2010))
Vargas-Irwin, C. E., Shakhnarovich, G., Yadollahpour, P., Mislow, J., Black, M. J., Donoghue, J. P.
J. of Neuroscience, 39(29):9659-9669, July 2010 (article)
Kjellstrom, H., Kragic, D., Black, M. J.
In IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, pages: 747-754, June 2010 (inproceedings)
Sun, D., Roth, S., Black, M. J.
In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 2432-2439, IEEE, June 2010 (inproceedings)
Sigal, L., Black, M. J.
International Journal of Computer Vision, 87(1):1-3, March 2010 (article)
Sigal, L., Balan, A., Black, M. J.
International Journal of Computer Vision, 87(1):4-27, Springer Netherlands, March 2010 (article)
Vargas-Irwin, C., Franquemont, L., Shakhnarovich, G., Yadollahpour, P., Black, M., Donoghue, J.
2010 Abstract Viewer and Itinerary Planner, Society for Neuroscience, 2010, Online (conference)
Sun, D., Sudderth, E., Black, M. J.
In Advances in Neural Information Processing Systems 23 (NIPS), pages: 2226-2234, MIT Press, 2010 (inproceedings)
Stanley, G., Jin, J., Wang, Y., Desbordes, G., Black, M., Alonso, J.
COSYNE, 2010 (conference)
Kim, S., Simeral, J. D., Hochberg, L. R., Donoghue, J. P., Black, M. J.
In 7th Asian Control Conference, ASCC09, pages: 988-993, Hong Kong, China, August 2009 (inproceedings)
Roth, S., Black, M. J.
International Journal of Computer Vision (IJCV), 82(2):205-29, April 2009 (article)
Guan, P., Weiss, A., Balan, A., Black, M. J.
In Int. Conf. on Computer Vision, ICCV, pages: 1381-1388, 2009 (inproceedings)
Vargas-Irwin, C. E., Yadollahpour, P., Shakhnarovich, G., Black, M. J., Donoghue, J. P.
2009 Abstract Viewer and Itinerary Planner. Society for Neuroscience, Society for Neuroscience, 2009, Online (conference)
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