Frontiers of Engineering Reports on Leading-Edge Engineering from the 2007 Symposium (2008) / Chapter Skim
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Computational Cognitive Neuroscience and Its Applications--Laurent Itti
Computational Cognitive Neuroscience and Its Applications--Laurent Itti
Pages 87-98
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From page 87... ...
. An everyday demonstration of this state of affairs is the use of simple image, character, or sound recognition in CAPTCHA tests (completely automated public Turing tests to tell computers and humans apart; see von Ahn et al., 2004)
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From page 88... ...
Taking inspiration from nature, recent work in computational neuroscience has hence started to devise a new breed of algorithms, which can be more flexible, robust, and adaptive when confronted with the complexities of the real world. I focus here on describing recent progress with a few simple examples of such algorithms, concerned with directing attention toward interesting locations in a visual scene, so as to concentrate the deployment of computing resources primarily onto these locations.
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provide a very nice review of which elementary visual features may strongly contribute to visual salience and guide visual search. Two mathematical constructs can be derived from electrophysiological recordings in living brains, which shed light onto how this detection of statistical odd man out may be carried out.
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in an array of items (bright-green bars) is highly salient and immediately and effortlessly grabs visual attention in a bottom-up, image-driven manner.
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All feature maps feed into a single saliency map that topographically represents salience irrespectively of features. Attention is first directed to the most salient location in the image, and subsequently to less salient locations.
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Related formulations of this basic principle have been expressed in slightly different terms, including defining salient locations as those that contain spatial outliers (Rosenholtz, 1999) , which may be more informative in Shannon's sense (Bruce and Tsotsos, 2006)
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Thus, our study demonstrates the advantages of integrating bottom-up factors derived from a saliency map and top-down factors learned from image and task contexts in predicting where humans look while performing complex visually guided behavior. In continuing work we are exploring ways of introducing additional domain knowledge into the top-down component of our attentional system.
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From page 94... ...
, the training phase, we compile a training set containing feature vectors and eye positions corresponding to individual frames from several video game clips that were recorded while observers interactively played the games. The feature vectors may be derived from either the Fourier transform of the image luminance; or dyadic pyramids for luminance, color, and orientation; or as a control condition, a random distribution.
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was computed from previously learned associations between scene gist and human gaze (location of maximum top-down activity indicated by small red circle)
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1985. Stimulus specific responses from beyond the classical receptive field: Neurophysiological mechanisms for local-global comparisons in visual neurons.
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1999. A simple saliency model predicts a number of motion popout phenomena.
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1994. Guided search 2.0: A revised model of visual search.
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