Computational modelling of visual attention.

by: L Itti, C Koch

Nat Rev Neurosci, Vol. 2, No. 3. (March 2001), pp. 194-203.

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Abstract

Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention.

@article{citeulike:150306, abstract = {Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention.}, address = {Hedco Neuroscience Building, University of Southern California, 3641 Watt Way, Los Angeles, California 90089-2520, USA. itti@usc.edu}, author = {Itti, L. and Koch, C. }, citeulike-article-id = {150306}, issn = {1471-003X}, journal = {Nat Rev Neurosci}, keywords = {\_note, attention, bottomup, review, salience}, month = {March}, number = {3}, pages = {194--203}, posted-at = {2006-11-10 21:36:56}, priority = {2}, title = {Computational modelling of visual attention.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/11256080}, volume = {2}, year = {2001} }

RIS record

TY - JOUR ID - citeulike:150306 L3 - citeulike-article-id:150306 TI - Computational modelling of visual attention. JF - Nat Rev Neurosci VL - 2 IS - 3 SP - 194 EP - 203 AD - Hedco Neuroscience Building, University of Southern California, 3641 Watt Way, Los Angeles, California 90089-2520, USA. itti@usc.edu SN - 1471-003X N2 - Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention. KW - _note KW - attention KW - bottomup KW - review KW - salience AU - Itti, L AU - Koch, C PY - 2001/03// UR - http://view.ncbi.nlm.nih.gov/pubmed/11256080 ER -

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