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Vision Systems for a Natural Human Environment

Objective

The aim of INSIGHT II is to improve our understanding of vision at the computational level, that is, to identify the representations to be used and the algorithms to compute them for general-purpose vision systems operating in a fluctuating natural environment.
The site of convergence of luminance, motion and texture cues specifying shape in the primate brain has been established.

New insights into the receptive field structure of higher order visual neurons and on the integration of visual and eye movement signals in the primate brain has been achieved.

On the psychophysical side, a new technique has been developed for the measurement of subjects' perception of surfaces. A wealth of new data on the estimation by humans of surface position, orientation and curvature specified by disparity, motion, luminance, texture and other cues has been obtained.

On the computational side, a new theory has been developed on the recovery of surfaces from the structure of the disparity field.

In computer vision, mathematical theory, implementation and experimentation have been combined to estimate optic flow and its use for recovering object 3-dimensional structure and 3-dimensional motion.
APPROACH AND METHODS

Using the results of a previous action (INSIGHT, 3001) as a guide, a number of key areas have been identified, mainly in middle-level vision, where rapid progress can be expected. These areas include the measurement of optic flow, cue integration, the task-dependent use of optic flow, and surface and object representation. These five topics are approached from a multidisciplinary point of view combining psychophysics and neurophysiology in studying biological visual system in primates, and computer science in studying computer vision. While psychophysics studies human or primate performance and conjectures about underlying mechanisms, physiology measures these mechanisms directly. The strategies employed by the most successful computer (the primate brain) will thus be confronted with those used in computer vision to solve similar problems.

POTENTIAL

The vision knowledge gained from these fundamental studies will give firm ground to more applied projects in which vision is used to guide robots or recognise patterns.

Coordinator

KATHOLIEKE UNIVERSITEIT LEUVEN
Address
Tervuursevest, 101
3000 Leuven
Belgium

Participants (13)

COMPUTER TECHNOLOGY INSTITUTE
Greece
Address
Kolokotroni Street, 326110
26221 Patras
Institut National de Recherches en Informatique et en Automatique (INRIA)
France
Address
Domaine De Voluceau Rocquencourt
78153 Le Chesnay
RUHR-UNIVERSITY BOCHUM
Germany
Address
Universitätsstraße 150
44780 Bochum
Royal Institute of Technology
Sweden
Address

SE 100 44 Stockholm
UNIV CATHOLIQUE DE LOUVAIN, ESAT
Belgium
Address

Leuven
UNIVERSITAET ZURICH
Switzerland
Address
Frauenklinikstrasse, 26
8091 Zurich
UNIVERSITE DE NICE - SOPHIA ANTIPOLIS
France
Address
Parc Valrose
06034 Nice
UNIVERSITEIT VAN UTRECHT
Netherlands
Address
Princetonpleinweg 5, 80000
3508 TA Utrecht
UNIVERSITY OF GENOVA
Italy
Address
Via Dodecaneso 35
16146 Genova
UNIVERSITÄT KARLSRUHE
Germany
Address
Am Zirkel 2
Karlsruhe
University of Oxford
United Kingdom
Address
11 Keble Road
OX1 3QD Oxford
University of Sheffield
United Kingdom
Address
Western Bank
S10 2TN Sheffield
University of Stirling
United Kingdom
Address

FK9 4LA Stirling