Adaptive vision leveraging digital retinas: extracting meaningful segments

Authors:
Nicolas Burrus;Thierry M. Bernard
Affiliations:
ENSTA / UEI, Paris, France;ENSTA / UEI, Paris, France
Venue:
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Year:
2006

Citing 6
Cited 1

Meaningful Alignments

International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
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Vision Chips
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Abstract

In general, the less probable an event, the more attention we pay to it. Likewise, considering visual perception, it is interesting to regard important image features as those that most depart from randomness. This statistical approach has recently led to the development of adaptive and parameterless algorithms for image analysis. However, they require computer-intensive statistical measurements. Digital retinas, with their massively parallel and collective computing capababilities, seem adapted to such computational tasks. These principles and opportunities are investigated here through a case study: extracting meaningful segments from an image.