Image Segmentation with Networks of Variable States

Jan Ben We developed a neural net architecture for segmenting complex images, i.e., to localize two-dimensional geometrical shapes in a scene, without prior knowledge of the objects' positions and sizes. A scale variation is built into the network to deal with varying sizes. This algorithm has been applied to video images of railroad cars, to find their identification numbers. Over 95% of the characlers were located correctly in a data base of 300 images, despile a large variation in lighting conditions and often a poor quality of the characters. A part of the network is executed on a processor board containing an analog neural net chip (Graf et aI. 1991). while the rest is implemented as a software model on a workstation or a digital signal processor.