Hosted at the 5th International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS 2010)

Boston, MA, USA December 1 - 3, 2010 http://www.bionetics.org/sp/bimv.html

While machine vision systems are becoming increasingly powerful, in most regards they are still far inferior to their biological counterparts. For instance, in terms of object segmentation, recognition of object categories, viewpoint and lighting invariance, or material recognition, much can be learned from the visual systems of humans and animals. Studying the biological systems and applying the findings to the construction of computational vision models and artificial vision systems is therefore a promising way of advancing the field of machine vision. Conversely, evaluating the performance of such models and systems in comparison to the biological systems can provide important feedback for a better understanding of the brain mechanisms underlying natural vision. Bio-inspired machine vision is thus a truly interdisciplinary research endeavor that benefits all scientific disciplines involved.

The objective of the Special Track on Bio-Inspired Machine Vision is to bring together scientists from fields such as computer science, engineering, psychology, neuroscience, and biology to discuss their current work relating to this research effort. This track will provide an opportunity for exchanging research ideas and initiating cross-disciplinary research partnerships that will lead to novel approaches and developments in both machine vision and the study of biological vision. To achieve its objective, this special track seeks high-quality, original and unpublished papers addressing topics relevant to the issues raised above, for example:

• Psychophysical, neuroimaging, EEG, or TMS studies of the human visual system that are relevant to computational vision models or machine vision applications
• Studies of vision in animals with results that apply to computational models or technical applications in vision
• Implementation and evaluation of biologically inspired components in machine vision systems
• Systematic evaluation of biologically inspired artificial vision systems and comparison of the results to behavioral or neurophysiological data
• Biologically motivated computational models of specific aspects of biological vision that are relevant to technical vision applications
• Evaluation of human or animal vision with regard to ideal observer models
• Studies of visual attention in humans or animals that may inform the implementation of attentional mechanisms in technical vision systems
• Implementation and evaluation of mechanisms of location-, feature-, or object-based attention in artificial vision systems or computational models of vision
• Construction of active vision systems with camera control inspired by human eye-movement behavior

Paper Submission:

Authors are invited to submit papers in the following categories:

• Regular papers: Up to 15 pages
• Short papers: Up to 2 pages
• Work-in-progress papers: Up to 6 pages
• Demo papers: Up to 4 pages

Papers must follow the Springer LNICST format. Please visit http://www.bionetics.org/ submission.shtml for detailed submission instructions.

Important Dates:

• Regular paper submission due: July 30
• Short, work-in-progress and demo paper submission due: September 19
• Notification of acceptance for regular papers: September 12
• Notification of acceptance for short, work-in-progress and demo papers: September 30
• Camera ready due: October 10

Publication:

All accepted paper will be published by Springer. A selected number of best papers will be considered for publication in leading journals such as ACM Trans. on Autonomous and Adaptive Systems.

Program Committee Members:

• Erhardt Barth, University of Lubeck, Germany
• Neil Bruce, York University, Toronto, Canada
• Sharat Chikkerur, Massachusetts Institute of Technology, Cambridge, MA
• David Cox, Harvard University, Cambridge, MA
• Michael Dorr, Schepens Eye Research Institute, Boston, MA
• Tyler Garaas, Mitsubishi Electric Research Laboratories, Cambridge, MA
• Nurit Haspel, University of Massachusetts at Boston, Boston, MA
• Alex Hwang, Schepens Eye Research Institute, Boston, MA
• Gang Luo, Schepens Eye Research Institute, Boston, MA
• Ronaldo Menezes, Florida Institute of Technology, Melbourne, FL
• Marc Pomplun, University of Massachusetts at Boston, Boston, MA
• Eraldo Ribeiro, Florida Institute of Technology, Melbourne, FL

Track Chairs:

• Marc Pomplun, University of Massachusetts at Boston, Boston, MA
• Tyler Garaas, Mitsubishi Electric Research Laboratories, Cambridge, MA