Abstract

INTRODUCTION Pictorial pattern recognition systems are often described as consisting of three parts: a preprocessing part, a feature extraction part, and a classification part. The preprocessing is used to enhance or sharpen the image to be processed. This is usually done using linear operations or operations on the gray scale such as thresholding [1-3] The classification part is fairly well understood [4,5]. The feature extractor, on the other hand, is very much dependent upon the actual problem and no general theory has emerged on how to deal with it. Feature extraction procedures so far have been ad hoc, often referred to as "a bag of tricks." The present work grew out of an interest in finding a single picture operator that could in parallel perform a number of useful operations and that could work on several levels in a hierarchy. One background to this interest is the feeling that the eyes and brains of humans and animals are likely to have such standard operators, as the micro

Citations

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