Home /  Emphasis Week on Learning and Inference in Low and Mid Level Vision


Emphasis Week on Learning and Inference in Low and Mid Level Vision February 21, 2005 - February 25, 2005
Registration Deadline: January 03, 2005 over 19 years ago
To apply for Funding you must register by: November 21, 2004 over 19 years ago
Parent Program:
Organizers Andrew Blake and Yair Weiss

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Note: All lectures are to be held in the MSRI lecture hall 2850 Telegraph Avenue, second floor. Low level vision addresses the issues of labelling and organising image pixels according to scene related properties - known as intrinsic images - such as motion, contrast, relief, texture and reflectance. Such properties are hard to capture by hand-constructed models, and so there has been a considerable movement towards specifying them "generatively": models involving cascades of random variables are constructed as explanations of images. Details of the models are filled in by learning parameters from labelled training data. New inference algorithms such belief propagation, variational inference, particle filtering, min cut and others are used to apply these models to image analysis. Already some very promising results have been obtained, for example in segmentation, in stereo vision and in analysis of texture. This workshop will be a forum for some of the latest results and thinking in this area to be presented and explored. Program Schedule Monday 21st Feb 2.30-3.30: Prof Eero Simoncelli, New York University. Image statistics, efficient coding, and visual perception 4.15-5.15: Prof David Mumford, Brown University. Is there a simple statistical model of generic natural images? Tuesday 22nd Feb 09.30-10.30: Prof Michael Black, Brown University. Image statistics and low level vision 11.00-12.00: Dr Andrew Fitzgibbon, U. Oxford. Applied natural image statistics 2.00-3.00: Prof Alyosha Efros, CMU. Data-driven vision: learning by lookup 3.30-4.30: Prof Brendan Frey, U. Toronto. Using data-based parameterizations to efficient learn hierarchical models. Wednesday 23rd Feb 09.30-10.30: Prof Bill Freeman, MIT. Learning to separate shading from paint 11.00-12.00: Prof Song Chun Zhu, UCLA. From primal sketch to 2 1/2 Sketch -- shape from shading, stereo, and motion 2.00-3.00: Prof Andrew Blake, Microsoft Research. Fusion of colour, contrast and stereo for bi-layer segmentation 3.30-4.30: Dr Michael Isard, Microsoft Research. Estimating stereo and optic flow using loopy belief propagation Thursday 24th Feb 09.30-10.30: Prof Daniel Huttenlocher, Cornell. Speeding up belief propagation for low and mid level vision 11.00-12.00: Prof Yair Weiss, Hebrew University. Linear programming belief propagation and low-level vision 1.30-2.30: Prof Alan Yuille, UCLA. Beyond BP? Approximate Inference and the DLR equations 3.00-4.00: MSRI mathematics seminar for those who wish to attend 4.30-5.30: Prof Ramin Zabih, Cornell. Graph cut energy minimization algorithms for computer vision Friday 25th Feb 09.30-10.30: Prof Julian Besag and Raphael Gottardo, U. Washington. Microarray imaging with MRFs and MCMC 11.00-12.00: Prof David Donoho, Stanford. Scaling properties of higher-order image statistics: implications for edge/object detection
Keywords and Mathematics Subject Classification (MSC)
Primary Mathematics Subject Classification No Primary AMS MSC
Secondary Mathematics Subject Classification No Secondary AMS MSC
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To apply for funding, you must register by the funding application deadline displayed above.

Students, recent PhDs, women, and members of underrepresented minorities are particularly encouraged to apply. Funding awards are typically made 6 weeks before the workshop begins. Requests received after the funding deadline are considered only if additional funds become available.

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For information about recommended hotels for visits of under 30 days, visit Short-Term Housing. Questions? Contact coord@slmath.org.

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Schedule, Notes/Handouts & Videos
Show Schedule, Notes/Handouts & Videos
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Feb 21, 2005
02:30 PM - 03:30 PM
  Image Statistics, Efficent Coding and Visual Perception.
Eero Simoncelli
03:00 PM - 04:00 PM
  Is There Simple Statistical Model of Generic Natural Images?
David Mumford (Brown University)
Feb 22, 2005
11:00 AM - 12:00 PM
  Applied Natural Image Statistics
Andrew Fitzgibbon
02:00 PM - 03:00 PM
  Data-driven Vision: Learning by Lookup
Alexei Efros
03:30 PM - 04:30 PM
  Using Data-based Parameterizations to Efficient Learn Hierarchical Models.
Brendan Frey
Feb 23, 2005
12:00 AM - 12:00 AM
  From Primal Sketch to 2 1/2 Sketch -- Shape from Shading, Stereo, and Motion.
Song Chun Zhu (University of California, Los Angeles)
09:30 AM - 10:30 AM
  Image Satistics and Low Level Vision.
Michael Black
09:30 AM - 10:30 AM
  Learning to Separate Shading From Paint .
William Freeman
02:00 PM - 03:00 PM
  Fusion of Colour, Contrast and Stereo for Bi-layer Segmentation.
Andrew Blake
03:30 PM - 04:30 PM
  Estimating Stereo and Optic Flow Using Loopy Belief Propagation.
Michael Isard
Feb 24, 2005
12:00 AM - 12:00 PM
  Linear Programming, Belief Propagation and Low-level Vision.
Yair Weiss (The Hebrew University of Jerusalem)
09:30 AM - 10:30 AM
  Speeding up Belief Propagation for Low and Mid Level Vision
Dan Huttenlocher
01:30 PM - 02:30 PM
  Beyond BP? Approximate Inference and the DLR Equations.
Alan Yuille
04:30 PM - 05:30 PM
  Graph Cut Energy Minimization Algorithms for Computer Vision.
Ramin Zabih
Feb 25, 2005
09:30 AM - 10:30 AM
  Microarray Imaging with MRFs and MCMC .
Julian Besag, Raphael Gottardo
11:00 AM - 12:00 PM
  Scaling Properties of Higher-order Image Statistics: Implications for Edge/Object Detection.
David Donoho (Stanford University)