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 (Tsotsos, et al., 2002). 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
['Florentin Dorian Vintila', 'John K. Tsotsos']
Motion Estimation Using a General Purpose Neural Network Simulator for Visual Attention