Complex Networks: New Concepts and Tools for Real-Time Imaging and Vision

TL;DR

This paper explores how complex network concepts can enhance real-time imaging and vision by representing images, modeling visual saliency, and simulating parallel computing system performance.

Contribution

It introduces the application of complex networks to image representation, visual saliency modeling, and performance simulation of parallel computing in vision tasks.

Findings

Complex networks effectively model visual saliency.

Application of complex networks improves image characterization.

Modeling parallel systems aids in performance analysis.

Abstract

This article discusses how concepts and methods of complex networks can be applied to real-time imaging and computer vision. After a brief introduction of complex networks basic concepts, their use as means to represent and characterize images, as well as for modeling visual saliency, are briefly described. The possibility to apply complex networks in order to model and simulate the performance of parallel and distributed computing systems for performance of visual methods is also proposed.