Paper
7 June 2004 From low-level perception to high-level perception: a coherent approach for visual attention modeling
Olivier Le Meur, Patrick Le Callet, Dominique Barba, Dominique Thoreau, Edouard Francois
Author Affiliations +
Proceedings Volume 5292, Human Vision and Electronic Imaging IX; (2004) https://doi.org/10.1117/12.527440
Event: Electronic Imaging 2004, 2004, San Jose, California, United States
Abstract
The saliency-based or bottom-up model of visual attention presented in this paper deals with still color images. The model we built is based on numerous properties of the human visual system (HVS), thus providing a biologically plausible system. The computation of early visual features such as color and orientation is a key step for any bottom-up model and the way to extract these visual features easily permits to differentiate a model from an other. The novelty of the proposed approach lies on the fact that the computation of early visual features is fully based on a HVS model consisting in projecting the picture into an opponent-colors space, applying a perceptual decomposition, contrast sensitivity and masking functions. Moreover, a strategy essentially based on a center surround mechanism and on the perceptual grouping phenomena underscores conspicuous locations by combining visual feature maps. A saliency map which is defined as a 2D topographic representation of conspicuity is then deduced. The model is applied to a number of natural images. Our results are then compared with the results of a well-know bottom-up model.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Olivier Le Meur, Patrick Le Callet, Dominique Barba, Dominique Thoreau, and Edouard Francois "From low-level perception to high-level perception: a coherent approach for visual attention modeling", Proc. SPIE 5292, Human Vision and Electronic Imaging IX, (7 June 2004); https://doi.org/10.1117/12.527440
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CITATIONS
Cited by 14 scholarly publications and 2 patents.
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KEYWORDS
Visualization

Visual process modeling

Colorimetry

Visibility

Spatial frequencies

Eye models

Eye

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