Selecting salient objects in real scenes: an oscillatory correlation model.
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.
Quiles
Marcos G
MG
Department of Science and Technology, Federal University of São Paulo (Unifesp), São José dos Campos, SP, Brazil. quiles@unifesp.br
Wang
DeLiang
D
Zhao
Liang
L
Romero
Roseli A F
RA
Huang
De-Shuang
DS
D001288:Attention / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D003952:Diagnostic Imaging; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation