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• W2006108091 abstract "Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real gray-level and color images and the simulation results show the effectiveness of the system." @default.
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• W2006108091 date "2011-01-01" @default.
• W2006108091 modified "2023-10-18" @default.
• W2006108091 title "Selecting salient objects in real scenes: An oscillatory correlation model" @default.
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• W2006108091 doi "https://doi.org/10.1016/j.neunet.2010.09.002" @default.
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