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4518CFP: BICT 2014 Special Track on Bio-Inspired Machine Vision (BIMV)

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  • Lee Chonho (Dr)
    Jul 2, 2014
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      CFP: BICT 2014 Special Track on Bio-Inspired Machine Vision (BIMV)

      8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

      December 1 (Mon) - December 3, 2014 (Wed)
      Boston, MA, USA

      In-corporation with ACM

      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 To achieve its objective, this special track seeks high-quality, original and unpublished papers addressing topics relevant to the issues raised above, for example: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
      * Deep learning approaches to machine vision
      * 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


      Authors are invited to submit regular papers (up to 8 pages each), short papers (up to 4 pages each) or poster/demo papers (up to 2 pages each) in ACM's paper template. Up to two extra pages are allowed for each paper with extra page charges. See http://bionetics.org/2014/show/initial-submission for more details.  


      All accepted paper will be published through ACM Digital Library and submitted for indexing by SI, EI Compendex, Scopus, ACM Library, Google Scholar and many more. Selected papers will be considered for publication in leading journals including:

      * ACM/Springer Mobile Networks and Applications
      * Elsevier Information Sciences
      * Elsevier Nano Communication Networks Journal
      * Int'l Journal of Software Engineering and Knowledge Engineering
      * Cloud-integrated Cyber-Physical Systems (Springer book) 


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

      PC MEMBERS: 

      Erhardt Barth, University of Luebeck, Germany
      Vincent Courboulay, Université de La Rochelle, France
      David Fofi, University of Burgundy, France
      Tyler Garaas, Mitsubishi Electric Research Labs, Cambridge, MA
      Nurit Haspel, University of Massachusetts Boston, MA
      Pierre Kornprobst, INRIA, Sophia-Antipolis, France
      Martin Lages, University of Glasgow, Scotland
      Gang Luo, Schepens Eye Research Institute, Boston, MA
      Pabitra Mitra, Indian Institute of Technology, Kharagpur, India
      Heiko Neumann, University of Ulm, Germany
      Rajarshi Pal, Indian Institute of Technology, Kharagpur, India
      Marc Pomplun, University of Massachusetts Boston, MA
      Florian Raudies, Boston University, Boston, MA
      Antonio Rodriguez-Sanchez, University of Innsbruck, Austria
      Samuel Romero-Garcia, University of Granada, Spain
      Silvio Sabatini, University of Genoa, Italy
      AbdElrahman Shabayek, Suez Canal University, Egypt

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