Motion estimation using a general purpose neural network simulator for visual attention

Abstract

Motion detection and estimation is a first step in the much larger framework of attending to visual motion based on Selective Tuning Model of Visual Attention [1]. In order to be able to detect and estimate complex motion in a hierarchical system it is necessary to use robust and efficient methods which encapsulate as much information as possible about the motion together with a measure of reliability of that information. One such method is the orientation tensor formalism which incorporates a confidence measure that propagates into subsequent processing steps. The tensor method is implemented in a neural network simulator which allows distributed processing and visualization of results. As output we obtain information about the moving objects from the scene. © 2007 IEEE.