Estimating Motion Parameters with Three-Dimensional Self-Organizing Maps

Abstract The importance of analyzing moving scenes within the wide area of digital image processing is increasingly high. Although a simple detection of object velocity by neural networks has been considered in previously published papers, an implementation of artificial neural networks using a priori information for motion analysis is still quite rare. This paper shows the benefits from artificial neural networks, and from using a priori information about the contents of the history in the image sequence to improve the accuracy and speed of estimating motion parameters in the cases of distorted or overlapped objects. In the first place, it introduces three-dimensional Self-Organizing Maps (SOM) with two-dimensional input layers.