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Introductory Workshop in Mathematical, Computational and Statistical Aspects of Image Analysis January 24, 2005 - January 28, 2005
Registration Deadline: January 28, 2005 over 18 years ago
To apply for Funding you must register by: October 24, 2004 over 18 years ago
Parent Program:
Organizers David Donoho, Olivier Faugeras, David B Mumford

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The introductory workshop will be a week long and concentrate on problems in what is typically described as ``early vision'' or ``low-level vision''. By this, people mean whatever you can understand about an image as a function or a signal without introducing explicitly the origin of the image on the basis of the physics and the specific objects present in the world. We have in mind the following themes, each of which will be introduced in a series of tutorial lectures, intended at a level that could be understood by mathematicians, physical scientists or engineers with no previous background in vision and image analysis. 1. Harmonic analysis applied to images. The last few years have seen a great deal interest in the computational harmonic analysis community on developing approximations and expansions specifically oriented to problems in dealing with images, for example edges and textures in images. The resulting multiscale processing tools start with wavelets and go considerably beyond (bandelets, curvelets, ridgelets, brushlets, etc.). There are numerous applications to compression, denoising etc. (Candes, Mallat, Meyer, Saito, Donoho) 2. Statistics of natural images at the signal as well as morphological levels. Because of its data intensive nature, a deep study of the statistics of images lagged some 20 years behind the statistical study of speech. However, many groups are now working out many types of statistics and constructing stochastic models for various aspects of natural images. (Malik, Grenander, Ruderman, Simoncelli, Olshausen, Gousseau, Lee, van Hateren, Freeman, Mumford) 3. Contours, textures, and perceptual organization. The gestalt school of psychophysics, from the 20.s through the 60.s, systematized in a qualitative way the rules by which the elements of images are grouped into larger structures. Vision scientists are now beginning to formalize these rules quantitatively. (Malik, Zhu, Morel, Moisan, Desolneux, Geman, Williams). 4. Variational approaches, partial differential equations for image analysis. These techniques date from the 80.s (Mumford-Shah/Blake-Zisserman functional, the .snakes. of Terzopoulos, Perona-Malik non-linear diffusion) and have been one of the main mathematical approaches to image processing, esp. in the schools of Osher and Morel. (Osher, Chan, Shah, Tannenbaum, Morel, Guichard, Faugeras, Mumford, Sethian). Lectures Tutorial/Introductory/Survey David Donoho (Stanford) 1. Natural Image Statistics and Bayesian Statistics, Information Theory vs. Computer Vision Perspectives 2. Image Manifolds and Image Complexes 3. Harmonic Analysis Analogies to Early Vision. Olivier Faugeras (INRIA) 1. Fundamental PDE's of Computer Vision 2. Approaches to Image Warping and Matching 3. Shape Topologies and Applications to Segmentation David Mumford (Brown) 1. Pattern theory: Grenander's ideas and examples. 2. Modeling shape: comparing metrics, L^1, L^2 and L^\infty techniques, the solid, liquid and conformal approaches. Research/Advanced 1. Image representation: Eero Simoncelli (NYU) 2. Biological vision: Bruno Olshausen (Davis/RNI) 3. Seeing as Statistical Inference: Song Chun Zhu (UCLA) 4. Statistics of Grouping and Figure/Ground in Natural images: J.Malik 5. Modern Classifier design: Trevor Hastie (Stanford) 6. Towards Unsupervised Learning of Categories: Pietro Perona (Caltech) 7. Strategies for visual recognition: Donald Geman (JHU/ENS Cachan) 8. Ecological optics: Jan Koenderink 9. Energy minimization and "u+v" models: Luminita Vese (UCLA) Schedule of Talks
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 Ph.D.'s, 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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MSRI does not hire an outside company to make hotel reservations for our workshop participants, or share the names and email addresses of our participants with an outside party. If you are contacted by a business that claims to represent MSRI and offers to book a hotel room for you, it is likely a scam. Please do not accept their services.

MSRI has preferred rates at the Hotel Shattuck Plaza, depending on room availability. Guests can call the hotel's main line at 510-845-7300 and ask for the MSRI- Mathematical Science Research Institute discount. To book online visit this page (the MSRI rate will automatically be applied).

MSRI has preferred rates at the Graduate Berkeley, depending on room availability. Reservations may be made by calling 510-845-8981. When making reservations, guests must request the MSRI preferred rate. Enter in the Promo Code MSRI123 (this code is not case sensitive).

MSRI has preferred rates at the Berkeley Lab Guest House, depending on room availability. Reservations may be made by calling 510-495-8000 or directly on their website. Select "Affiliated with the Space Sciences Lab, Lawrence Hall of Science or MSRI." When prompted for your UC Contact/Host, please list Chris Marshall (coord@msri.org).

MSRI has a preferred rates at Easton Hall and Gibbs Hall, depending on room availability. Guests can call the Reservations line at 510-204-0732 and ask for the MSRI- Mathematical Science Research Inst. rate. To book online visit this page, select "Request a Reservation" choose the dates you would like to stay and enter the code MSRI (this code is not case sensitive).

Additional lodging options may be found on our short term housing page.

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Schedule, Notes/Handouts & Videos
Show Schedule, Notes/Handouts & Videos
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Jan 24, 2005
09:00 AM - 10:00 AM
  Pattern Theory: Grenander's Ideas and Examples
David Mumford (Brown University)
10:30 AM - 11:30 AM
  Modern Classifier Design
Trevor Hastie
10:30 AM - 11:30 AM
  Variational Principles and PDE's of Computer Vision
Olivier Faugeras (Institut National de Recherche en Informatique Automatique (INRIA))
Jan 25, 2005
09:00 AM - 10:00 AM
  An Invitation to Visual Recognition
Pietro Perona
10:30 AM - 11:30 AM
  Strategies for Visual Recognition
Donald Geman (Johns Hopkins University)
01:30 PM - 02:30 PM
  Image Statistics and Surface Perception
Edward Adelson
03:00 PM - 04:00 PM
  Multiscale Geometric Analysis for Images
Richard Baraniuk
Jan 26, 2005
09:00 AM - 10:00 AM
  Modeling Shape
David Mumford (Brown University)
10:30 AM - 11:30 AM
  Variational Methods for Multimodal Image Matching: Theory and Applications
Olivier Faugeras (Institut National de Recherche en Informatique Automatique (INRIA))
01:30 PM - 02:30 PM
  Appearance Manifolds 2
David Donoho (Stanford University)
03:00 PM - 04:00 PM
  Energy Minimization for Cartoon & Texture Separation :U+V Models
Luminita Vese
Jan 27, 2005
09:00 AM - 10:00 AM
  Statistical Image Models
Eero Simoncelli
10:30 AM - 11:30 AM
  What We Know and Don't Know About Biological Vision
Bruno Olshausen (University of California, Berkeley)
01:30 PM - 02:30 PM
  Seeing as Statistical Inference
Song Chun Zhu (University of California, Los Angeles)
03:00 PM - 04:00 PM
  Ecological Statistics of Grouping and Figure-Ground Cues
Jitendra Malik (University of California, Berkeley)
Jan 28, 2005
09:00 AM - 10:00 AM
  Ecological Optics
Jan Koenderink
10:30 AM - 11:30 AM
  Variations on Image and Shape Warping, Statistics and Segmentation
Olivier Faugeras (Institut National de Recherche en Informatique Automatique (INRIA))
01:30 PM - 02:30 PM
  Learning and Image Segmentation
Joachim Buhmann
03:30 PM - 04:30 PM
  More Interactions
David Donoho (Stanford University)