Adaptive Vision Leveraging Digital Retinas: Extracting Meaningful Segments

  • Nicolas Burrus
  • Thierry M. Bernard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)


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.


False Alarm Direction Image Random Environment Gradient Magnitude White Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nicolas Burrus
    • 1
  • Thierry M. Bernard
    • 1
  1. 1.ENSTA / UEIParisFrance

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