International Conference on VISION, RECOGNITION, ACTION
CAs/CNS
cas-cns at cns.bu.edu
Fri Oct 4 14:24:57 EDT 1996
***** CALL FOR PAPERS *****
International Conference on
VISION, RECOGNITION, ACTION: NEURAL MODELS OF MIND AND MACHINE
May 28-31, 1997
Sponsored by the
Center for Adaptive Systems
and the
Department of Cognitive and Neural Systems
Boston University
with financial support from
the Defense Advanced Research Projects Agency
and
the Office of Naval Research
This conference will include a day of tutorials (May 28) followed by 3
days of 21 invited lectures and contributed lectures and posters by
experts on the biology and technology of how the brain and other
intelligent systems see, understand, and act upon a changing world.
Meeting updates can be found at http://cns-web.bu.edu/cns-meeting/.
Hotel and restaurant information can also be found here.
CONFIRMED INVITED SPEAKERS AND PROGRAM OUTLINE
WEDNESDAY, MAY 28, 1997
TUTORIALS
STEPHEN GROSSBERG
"Vision, Brain, and Technology"
(3 hours in two 1-1/2 hour lectures)
This tutorial will provide a self-contained introduction to recent
models of how the brain sees. It will also illustrate how these models
have been used to help solve difficult image processing problems in
technology. The biological part will discuss neural models of visual
form, color, depth, figure-ground separation, motion, and attention,
and how these several processes cooperate to generate complex
percepts. The tutorial will build a theoretical bridge between data
about visual perception and data about the architecture and dynamics
of the visual brain. Technological applications to image restoration,
texture labeling, figure-ground separation, and related problems will
be described.
GAIL CARPENTER
"Self-Organizing Neural Networks for Learning, Recognition, and
Prediction: ART Architectures and Applications"
(2 hours)
In 1976, Stephen Grossberg introduced adaptive resonance as a theory
of human cognitive information processing. Over the past decade, the
theory has led to an evolving series of real-time neural networks (ART
models) that self-organize recognition categories in response to
arbitrary sequences of input patterns. The intrinsic stability of an
ART system allows rapid learning of new information while essential
components of previously learned patterns are preserved. This tutorial
will describe basic ART design principles, analytic tools, and
benchmark simulations. Both unsupervised networks such as ART 1, ART
2, ART 3, and fuzzy ART, and supervised learning architectures such as
ARTMAP, fuzzy ARTMAP, and ART-EMAP will be discussed. Successful
applications of the ART and ARTMAP networks, including the Boeing
parts retrieval CAD system, automatic mapping from remote sensing
satellite measurements, and medical database prediction will be
outlined. Computational elements of the recently developed dART and
dARTMAP networks, that feature distributed code representations, will
also be introduced.
ERIC SCHWARTZ
"Algorithms and Hardware for the Application of Space-Variant
Active Vision to High Performance Machine Vision"
(2 hours)
The term space-variance refers to the fact that all higher vertebrate
visual systems are based on spatial architectures which have
non-constant resolution across the visual field. It has been shown
that such architectures can lead to up to four orders of magnitude of
compression in the space-complexity of vision tasks. However, there
are fundamental algorithmic and hardware problems involved in the
exploitation of these observations in computer vision, many of which
have benefited from considerable progress during the past several
years. In this tutorial, a brief outline of the anatomical basis for
the notion of space-variance will be provided. Several examples of
space-variant active vision systems will be then be discussed,
focusing on the hardware specifications for sensors, optics, actuators
and DSP based parallel processors. Finally, a review of the
algorithmic aspects of these systems will be presented, including
issues related to early vision (i.e., edge enhancement via nonlinear
diffusion methods), and to pattern matching, based on recent
development of an exponential chirp algorithm which can perform
high-speed quasi-shift invariant processing on logarithmic image
architectures. Functioning examples of space-variant active vision
systems based on these developments will be demonstrated, included a
miniature visually guided autonomous vehicle, a machine vision system
for reading license plates of high-speed vehicles for traffic control,
and a blind-prosthetic device based on a "wearable" active vision
system.
********************
TUTORIAL BIOSKETCHES:
GAIL CARPENTER is professor in the departments of Cognitive and Neural
Systems (CNS) and Mathematics at Boston University. She is the CNS
Director of Graduate Studies; 1989 Vice-President and 1994-96
Secretary of the International Neural Network Society (INNS);
organization chair of the 1988 INNS annual meeting; and a member of
the editorial boards of Brain Research, IEEE Transactions on Neural
Networks, Neural Computation, and Neural Networks. She has served on
the INNS Board of Governors since its founding in 1987, and is a
member of the Council of the American Mathematical Society. She is a
leading architect of the Adaptive Resonance Theory (ART) family of
architectures for fast learning, pattern recognition, and prediction
of nonstationary databases, including both unsupervised (ART 1, ART 2,
ART 2-A, ART 3, fuzzy ART, distributed ART) and supervised (ARTMAP,
fuzzy ARTMAP, ART-EMAP, distributed ARTMAP) ART networks. These
systems have been used for a wide range of applications, such as
medical diagnosis, remote sensing, automatic target recognition,
mobile robots, and database management. Her earlier research includes
the development, computational analysis, and applications of neural
models of nerve impulse generation (Hodgkin-Huxley equations),
vision, cardiac rhythms, and circadian rhythms. Professor Carpenter
received her graduate training in mathematics at the University of
Wisconsin and was a faculty member at MIT and Northeastern University
before moving to Boston University.
STEPHEN GROSSBERG is Wang Professor of Cognitive and Neural Systems
and Professor of Mathematics, Psychology, and Biomedical Engineering
at Boston University. He is the founder and Director of the Center
for Adaptive Systems, as well as the founder and Chairman the
Department of Cognitive and Neural Systems. He founded and was first
President of the International Neural Network Society and also founded
and is co-editor-in-chief of the Society's journal, Neural Networks.
Grossberg was General Chairman of the first IEEE International
Conference on Neural Networks. He is on the editorial boards of Brain
Research, Journal of Cognitive Neuroscience, Behavioral and Brain
Sciences, Neural Computation, IEEE Transactions on Neural Networks,
and Adaptive Behavior. He organized two multi-institutional
Congressional Centers of Excellence for research on biological neural
networks and their technological applications. He received the IEEE
Neural Network Pioneer award, the INNS Leadership Award, the Thinking
Technology Award of the Boston Computer Society, and is a Fellow of
the American Psychological Association and the Society of Experimental
Psychologists. Grossberg and his colleagues have pioneered and
developed a number of the fundamental principles, mechanisms, and
architectures that form the foundation for contemporary neural network
research. This work focuses upon the design principles and mechanisms
which enable the behavior of individuals to adapt successfully in
real-time to unexpected environmental changes. Core models pioneered
by this approach include competitive learning and self-organizing
feature maps, adaptive resonance theory, masking fields, gated dipole
opponent processes, associative outstars and instars, associative
avalanches, nonlinear cooperative-competitive feedback networks,
boundary contour and feature contour systems, and vector associative
maps. Grossberg received his graduate training at Stanford University
and Rockefeller University, and was a Professor at MIT before assuming
his present position at Boston University.
ERIC SCHWARTZ received the PhD degree in High Energy Physics from
Columbia University in 1973, followed by post-doctoral studies with
E. Roy John at New York Medical College in neurophysiology. He has
served as Associate Professor of Psychiatry at New York University
Medical Center and Associate Professor of Computer Science at the
Courant Institute of Mathematical Sciences. In 1985, he organized the
first Symposium on Computational Neuroscience, and in 1989 founded
Vision Applications, Inc. which designs and builds prototype machine
vision systems based on space-variant active vision
systems. Currently, he is Professor of Cognitive and Neural Systems,
Electrical Engineering and Computer Systems and Anatomy and
Neurobiology at Boston University. His research experience includes
experimental particle physics, physiology (single cell recording),
anatomy (2DG, PETT, MRI), computer graphics and image processing, VLSI
design, actuator design, and neural modeling.
*************************
THURSDAY, MAY 29, 1997
INVITED LECTURES
Robert Shapley, New York University:
Brain Mechanisms for Visual Perception of Occlusion
George Sperling, University of California, Irvine:
An Integrated Theory for Attentional Processes in Vision,
Recognition, and Memory
Patrick Cavanagh, Harvard University:
Direct Recognition
Stephen Grossberg, Boston University:
Perceptual Grouping and Attention during Cortical Form and Motion Processing
Robert Desimone, National Institute of Mental Health:
Neuronal Mechanisms of Visual Attention
Ennio Mingolla, Boston University:
Visual Search
Patricia Goldman-Rakic, Yale University Medical School:
The Machinery of Mind: Models from Neurobiology
Larry Squire, San Diego VA Medical Center:
Brain Systems for Recognition Memory
There will also be a contributed poster session on this day.
FRIDAY, MAY 30, 1997
INVITED LECTURES
Eric Schwartz, Boston University:
Multi-Scale Vortex Structure of the Brain:
Anatomy as Architecture in Biological and Machine Vision
Lance Optican, National Eye Institute:
Neural Control of Rapid Eye Movements
John Kalaska, University of Montreal:
Reaching to Visual Targets: Cerebral Cortical Neuronal Mechanisms
Rodney Brooks, Massachusetts Institute of Technology:
Models of Vision-Based Human Interaction
There will also be a contributed talk session and a reception,
followed by the
KEYNOTE LECTURE
Stuart Anstis, University of California, San Diego:
Moving in Unexpected Directions
SATURDAY, MAY 31, 1997
INVITED LECTURES
Azriel Rosenfeld, University of Maryland:
Some Viewpoints on Vision
Terrance Boult, Lehigh University:
Polarization Vision
Allen Waxman, MIT Lincoln Laboratory:
Opponent Color Models of Visible/IR Fusion for Color Night Vision
Gail Carpenter, Boston University:
Distributed Learning, Recognition, and Prediction in ART and ARTMAP Networks
Tomaso Poggio, Massachusetts Institute of Technology:
Representing Images for Visual Learning
Michael Jordan, Massachusetts Institute of Technology:
Graphical Models, Neural Networks, and Variational Approximations
Andreas Andreou, Johns Hopkins University:
Mixed Analog/Digital Neuromorphic VLSI for Sensory Systems
Takeo Kanade, Carnegie Mellon University:
Computational VLSI Sensors: Integrating Sensing and Processing
There will also be a contributed poster session on this day.
CALL FOR ABSTRACTS: Contributed abstracts by active modelers of
vision, recognition, or action in cognitive science, computational
neuroscience, artificial neural networks, artificial intelligence, and
neuromorphic engineering are welcome. They must be received, in
English, by January 31, 1997. Notification of acceptance will be given
by February 28, 1997. A meeting registration fee must accompany each
Abstract. See Registration Information below for details. The fee will
be returned if the Abstract is not accepted for presentation and
publication in the meeting proceedings.
Each Abstract should fit on one 8 x 11" white page with 1" margins on
all sides, single-column format, single-spaced, Times Roman or similar
font of 10 points or larger, printed on one side of the page only. Fax
submissions will not be accepted. Abstract title, author name(s),
affiliation(s), mailing, and email address(es) should begin each
Abstract. An accompanying cover letter should include: Full title of
Abstract, corresponding author and presenting author name, address,
telephone, fax, and email address. Preference for oral or poster
presentation should be noted. (Talks will be 15 minutes long. Posters
will be up for a full day. Overhead, slide, and VCR facilities will be
available for talks.) Abstracts which do not meet these requirements
or which are submitted with insufficient funds will be returned. The
original and 3 copies of each Abstract should be sent to: CNS Meeting,
c/o Cynthia Bradford, Boston University, Department of Cognitive and
Neural Systems, 677 Beacon Street, Boston, MA 02215.
The program committee will determine whether papers will be accepted
in an oral or poster presentation, or rejected.
REGISTRATION INFORMATION: Since seating at the meeting is limited,
early registration is recommended. To register, please fill out the
registration form below. Student registrations must be accompanied by
a letter of verification from a department chairperson or
faculty/research advisor. If accompanied by an Abstract or if paying
by check, mail to: CNS Meeting, c/o Cynthia Bradford, Boston
University, Department of Cognitive and Neural Systems, 677 Beacon
Street, Boston, MA 02215. If paying by credit card, mail to the above
address, or fax to (617) 353-7755.
STUDENT FELLOWSHIPS: A limited number of fellowships for PhD
candidates and postdoctoral fellows are available to at least
partially defray meeting travel and living costs. The deadline for
applying for fellowship support is January 31, 1997. Applicants will
be notifed by February 28, 1997. Each application should include the
applicant's CV, including name; mailing address; email address;
current student status; faculty or PhD research advisor's name,
address, and email address; relevant courses and other educational
data; and a list of research articles. A letter from the listed
faculty or PhD advisor on offiicial institutional stationery should
accompany the application and summarize how the candidate may benefit
from the meeting. Students who also submit an Abstract need to include
the registration fee with their Abstract. Reimbursement checks will be
distributed after the meeting. Their size will be determined by
student need and the availability of funds.
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REGISTRATION FORM
(Please Type or Print)
Vision, Recognition, Action: Neural Models of Mind and Machine
Boston University
Boston, Massachusetts
Tutorials: May 28, 1997
Meeting: May 29-31, 1997
Mr/Ms/Dr/Prof:
Name:
Affiliation:
Address:
City, State, Postal Code:
Phone and Fax:
Email:
The conference registration fee includes the meeting program,
reception, six coffee breaks, and the meeting proceedings. Two
coffee breaks and a book of tutorial viewgraph copies will be
covered by the tutorial registration fee.
CHECK ONE:
[ ] $55 Conference plus Tutorial (Regular)
[ ] $40 Conference plus Tutorial (Student)
[ ] $35 Conference Only (Regular)
[ ] $25 Conference Only (Student)
[ ] $30 Tutorial Only (Regular)
[ ] $25 Tutorial Only (Student)
Method of Payment:
[ ] Enclosed is a check made payable to "Boston University".
Checks must be made payable in US dollars and issued by a US
correspondent bank. Each registrant is responsible for any and
all bank charges.
[ ] I wish to pay my fees by credit card (MasterCard, Visa, or
Discover Card only).
Type of card:
Name as it appears on the card:
Account number:
Expiration date:
Signature and date:
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