Chaotic Phase Synchronization and Desynchronization in an Oscillator Network for Object Selection

TL;DR

This paper proposes a neural oscillator network model that uses chaotic phase synchronization to selectively identify salient objects in visual scenes, mimicking natural vision mechanisms.

Contribution

It introduces a novel object selection method based on phase synchronization of oscillators, with a shift mechanism for changing focus between objects.

Findings

Oscillator network successfully identifies salient objects.

Model exhibits behavior similar to natural visual attention.

Chaotic phase synchronization enables selective object extraction.

Abstract

Object selection refers to the mechanism of extracting objects of interest while ignoring other objects and background in a given visual scene. It is a fundamental issue for many computer vision and image analysis techniques and it is still a challenging task to artificial visual systems. Chaotic phase synchronization takes place in cases involving almost identical dynamical systems and it means that the phase difference between the systems is kept bounded over the time, while their amplitudes remain chaotic and may be uncorrelated. Instead of complete synchronization, phase synchronization is believed to be a mechanism for neural integration in brain. In this paper, an object selection model is proposed. Oscillators in the network representing the salient object in a given scene are phase synchronized, while no phase synchronization occurs for background objects. In this way, the salient object can be extracted. In this model, a shift mechanism is also introduced to change attention from one object to another. Computer simulations show that the model produces some results similar to those observed in natural vision systems.