title: Selecting salient objects in real scenes: an oscillatory correlation model.
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.
authors.affiliation: Department of Science and Technology, Federal University of São Paulo (Unifesp), São José dos Campos, SP, Brazil. quiles@unifesp.br
authors.forename: Marcos G
authors.initials: MG
authors.lastname: Quiles
authors.forename: DeLiang
authors.initials: D
authors.lastname: Wang
authors.forename: Liang
authors.initials: L
authors.lastname: Zhao
authors.forename: Roseli A F
authors.initials: RA
authors.lastname: Romero
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
mesh_terms: 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