ICVS 2007

Deadlines

2007-02-15

2006-11-20

20 Nov 2006

Submission

15 Feb 2007

Camera-Ready

organization

Bielefeld University

Metrics

Venue

Bielefeld University, Bielefeld, North Rhine-Westphalia, Germany

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The 5th International Conference on Computer Vision Systems will be held in 2007 at Bielefeld University, Germany on March 21st-24th continuing a series of successful events in Las Palmas, Spain, Vancouver, Canada, Graz, Austria, and New York, USA.
This conference aims to gather researchers and developers from academic fields and industries worldwide to share their research results covering all aspects of intelligent vision systems. The programme committee cordially invites you to attend ICVS 2007 and submit papers on all aspects of intelligent vision systems including,
but not limited to:
 Computer vision from a system perspective: applications, architecture, integration and control
 Cognitive vision techniques for scene analysis, semantic interpretation, and learning
 Methods and metrics for performance evaluation
A major theme will be vision systems that interact with or respond to their environment in a dynamic and
adaptive manner, with an emphasis put on integrated systems that are robust enough to be deployed in largely
unconstrained environments. Besides the main conference programme, workshops and tutorial will allow
practitioners building computer vision systems to exchange knowledge and ideas.
Papers will be reviewed by an international programme committee according to the following criteria:
 Pertinence: Does the paper describe methods or theories concerning the theme of the conference?
 Scientific quality: Does the paper clearly identify a scientific problem, document the state of the art and
demonstrate an original technique or method that resolves the problem?
 Impact: Is the method/model likely to be adopted for the design or evaluation of computer vision
systems?
 Generality: Can the method/model be used for a variety of problems? Is it non-specific?
 Innovation: Does the method/model demonstrate an improvement in the current state of the art?
	

This CfP was obtained from WikiCFP