Selecting salient objects in real scenes: An oscillatory correlation model
Quiles, Marcos G. [UNIFESP]
Romero, Roseli A. F.
Es parte deNeural Networks
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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. (C) 2010 Elsevier B.V. All rights reserved.
CitaNeural Networks. Oxford: Pergamon-Elsevier B.V., v. 24, n. 1, p. 54-64, 2011.
ResponsávelFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
K.C. Wong Education Foundation (Hong Kong)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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