Image Segmentation with Networks of Variable Scales

Part of Advances in Neural Information Processing Systems 4 (NIPS 1991)

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Hans Graf, Craig Nohl, 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 algo(cid:173) rithm 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 light(cid:173) ing 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.