Motion Estimation Using a General Purpose Neural Network Simulator for Visual Attention

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.

• Type: Conference
• Year: 2007
• Where: WACV
• Authors: Florentin Dorian Vintila, John K. Tsotsos
• Keywords: Orientation Tensor Formalism, Selective Tuning Model, Visual Motion, WACV 2007