NIPS_WORKSHOP

[Hayit Greenspan]

NIPS*93 - Post Meeting workshop:
    ---------------------------------------------------------------
    ---------------------------------------------------------------
       Learning in Computer Vision and Image Understanding -
              An advantage over classical techniques?

                           Dec 4th, 1993
    ---------------------------------------------------------------
    ---------------------------------------------------------------

Organizer:  Hayit Greenspan (hayit at micro.caltech.edu)
---------   Dept. of Electrical Engineering
            California Institute of Technology
            Pasadena, CA 91125

Program Committee: T. Poggio(MIT), R. Chellappa(Maryland), P. Smyth(JPL)
-----------------



Intended Audience: 
------------------- 
Researchers in the field of Learning and in Vision and those interested
in the combination of both for pattern-recognition, computer-vision and
image-understanding tasks.


Abstract:
---------
There is an increasing  interest in  the area  of Learning  in Computer
Vision and  Image Understanding, both from  researchers in the learning
community and from researchers involved with the computer vision world.
The field  is characterized by a shift  away from the classical, purely
model-based  computer vision  techniques, towards  data-driven learning
paradigms for solving real-world vision problems.

Classical  computer-vision techniques have to  a large extent neglected
learning, which is an  important  component  for  robust  and  flexible
vision  systems.  Meanwhile,  there is real-world  demand for automated
image  handling for scientific  and commercial purposes,  and a growing
need for  automated  image  understanding  and  recognition,  in  which
learning can play a key  role.    Applications  include  remote-sensing
imagery analysis, automated  inspection,  difficult  recognition  tasks
such as face  recognition,  autonomous  navigation  systems  which  use
vision as part of  their sensors,  and the  field of  automated imagery
data-base analysis.

Some of the issues for general discussion:
  o Where do classical computer-vision techniques  fail - and what are
    the main issues to be solved?
  o What  does learning  mean in  a vision  context?   Is it  tuning an
    existing  model (defined a priori) via its parameters, or trying to
    learn the model (extract most relevant features etc)?
  o Can existing learning techniques help in their present format or do
    we need vision-specific learning methods?  For example, is learning
    in vision  a practical prospect without  one "biasing" the learning
    models with lots of prior knowledge ?

The major  emphasis of the  workshop will be  on integrating viewpoints
from a  variety   of   backgrounds   (theory,   applications,   pattern
recognition,  computer-vision, learning, neurobiology).  The goal is to
forge some common ground between the different perspectives, and arrive
at a set of open questions and challenges in the field.





Program:
----------
  Morning session  
  ----------------
  7:30-7:35   Introduction to the workshop


  7:35-8:00   Keynote speaker: Poggio/Girosi (MIT) -  Learning and Vision


  8:00-8:15   Combining Geometric Reasoning and Artificial Neural Networks
              for Machine Vision
              Dean Pomerleau (CMU)

  8:15-8:45   Discussion:
              o AAAI forum on Machine Learning in Computer Vision-
                relevant issues,  Rich Zemel (Salk Institute)

              o What is going on in the vision and learning worlds


  8:45-8:55   Combining classical and learning-based approaches into a
              recognition framework for texture and shape
              Hayit Greenspan (Caltech)

  8:55-9:05   Visual Processing: Bag of tricks or Unified Theory?
              Jonathan Marshall (Univ. of N. Carolina)

  9:05-9:15   Learning in 3D object recognition- An extreme approach
              Bartlett Mel (Caltech)

  9:15-9:30   Discussion:
              o Learning in the 1D vs. 2D vs. 3D worlds 


  Afternoon session  
  -----------------
  4:30-4:45   The window registration problem in unsupervised
              learning of visual features
              Eric Saund (XEROX)

  4:45-4:55   Unsupervised learning of object models
              Chris Williams (Toronto)
 
  4:55-5:15   Discussion:
              o The role of unsupervised learning in vision


  5:15-5:30   Network architectures and learning algorithms for
              word reading 
              Yann Le Cun (AT&T)
 
  5:30-5:40   Challenges for vision and learning in the
              context of large scientific image databases 
              Padhraic Smyth (JPL)

  5:40-5:50   Elastic Matching and learning for face recognition 
              Joachim Buhmann (BONN)

  5:50-6:30   Discussion:
              o What are the difficult challenges in vision applications?

              o Summary of the main research objectives in the field today,
                as discussed in the workshop. 
-------------------------------------------------------------------------------