Structured computer vision
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FROM THE INTRODUCTION: Pictorial pattern recognition is a well-established and active field (1-3) with numerous applications in such areas as document processing (character recognition), industrial automation (assembly, inspec-tion), medicine (cytology, radiology), and remote sensing, among many others. Efficient picture recognition systems are thus a matter of great practical interest. However, relatively little is known about the computational theory of picture recognition. Given a class of digital pictures (i.e., arrays of numbers representing gray levels), and a class of allowable operations on pictures, one would like to be able to answer such questions as: (a) What types of recognition tasks are feasible? For what properties is it possible to determine whether or not a given picture has the given property? (b) What is the minimum number of operations (of the given type) required to perform a given recognition task (for pictures of a given size)? (c) How can the minimum required computation time be reduced by allowing sets of operations to be performed in parallel?
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