School of Electrical Engineering and Computer Science
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Marshall Tappen | |
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University of Central Florida School of Electrical Engineering and Computer Science | |
Contact Info:
Marshall Tappen
ENGR 3-230
Orlando, FL
Phone: (407) 823-2688
Email: mtappen
at cs.ucf.edu
BS - Brigham Young University - Provo, UT
SM - Massachusetts Institute of Technology - Cambridge, MA
PhD - Massachusetts Institute of Technology - Cambridge, MA
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2009 P. Scovanner and M.F. Tappen, Learning Pedestrian Dynamics from the Real World, To Appear in the 2009 International Conference on Computer Vision (ICCV) [PDF] K. G. G. Samuel and M.F. Tappen, Learning Optimized MAP Estimates in Continuously-Valued MRF Models. To appear in CVPR 2009 [PDF] 2008 M.F. Tappen, K. G.G. Samuel, C.V. Dean, and D. Lyle, The Logistic Random Field -- A Convenient Graphical Model for Learning Parameters for MRF-based Labeling. Appeared in the Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) Accepted for Oral Presentation [PDF] 2007 M. F. Tappen. “Utilizing Variational Optimization to Learn Markov Random Fields”. Appeared in The Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) [PDF] -- A sample training implementation is also available [.ZIP] M. F. Tappen, C. Liu, E. H. Adelson, and W. T. Freeman. “Learning Gaussian Conditional Random Fields for Low-Level Vision”. Appeared in The Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) [PDF] -- A sample training implementation is also available [.ZIP] 2006 and Earlier "Learning Continuous Models for Estimating Intrinsic Component
Images", Doctoral Thesis, Massachusetts Institute of Technology, May
2006 [PDF]
M. F. Tappen, E. H. Adelson, and W. T. Freeman. “Estimating
Intrinsic Component Images using Non-Linear Regression”.
Appeared in The Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Volume 2, Pages 1992-1999, 2006
,[PDF]
The training and test data from this paper is also available as a .MAT
file [.ZIP (10 MB)] A Comparative Study of Energy Minimization Methods for Markov Random
Fields. Rick Szeliski, Ramin Zabih, Daniel Scharstein, Olga Veksler,
Vladimir Kolmogorov, Aseem Agarwala, Marshall Tappen, Carsten Rother.
In Seventh European Conference on Computer Vision (ECCV 2002), volume 2, pages 16-29, Graz, May 2006. Springer-Verlag.
M. F. Tappen, W. T. Freeman, and E. H. Adelson. “Recovering
Intrinsic Images from a Single Image”. In IEEE Transactions
on Pattern Analysis and Machine Intelligence, Volume 27, Issue 9,
September 2005, Pages 1459 - 1472[PDF] M.F. Tappen, B. C. Russell, and W. T.
Freeman. “Efficient Graphical Models for Processing Images”.
The Proceedings of the 2004 IEEE Computer Society Conference on
Computer Vision and Pattern Recognition (CVPR), 2004 [PDF] M. F. Tappen and W. T. Freeman.
“Comparison of Graph Cuts with Belief Propagation for Stereo,
using Identical MRF Parameters”. In Proceedings of the Ninth
IEEE International Conference on Computer Vision (ICCV), Pages
900 - 907, 2003 [PDF] M. F. Tappen, B. C. Russell, and W. T.
Freeman. “Exploiting the Sparse Derivative Prior for
Super-Resolution and Image Demosaicing”. In Third
International Workshop on Statistical and Computational Theories of
Vision at ICCV 2003, 2003 [PDF] M. F. Tappen, W. T. Freeman, and E. H.
Adelson. “Recovering Intrinsic Images from a Single Image”.
In S. T. S. Becker and K. Obermayer, editors, Advances in Neural
Information Processing Systems 15, pages 1343-1350. MIT Press,
Cambridge, MA, 2003.[PDF] |
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| The latest Tappen family picture (but missing our latest member). I am fortunate to be married to the incomparable Joy Chan Tappen M.D. -- I married way up. | We're thrilled to have added a little girl to the house. But it took us a bit to get used to pink! |