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<p>The problem of uncertainty in image feature description is discussed, and it is shown how finite prolate spheroidal sequences can be used in the construction of feature descriptions that combine spatial and frequency-domain locality in an optimal way. Methods of constructing such optimal feature sets, which are suitable for graphical implementation, are described, and some generalizations of the quadtree concept are presented. These methods are illustrated by examples from image processing applications, including feature extraction and texture description. The problem of image segmentation is discussed, and the importance of scale invariance in overcoming the limitations imposed by uncertainty is demonstrated. A novel texture segmentation algorithm that is based on a combination of the new feature description and multiresolution techniques is described and shown to give accurate segmentations on a range of synthetic and natural textures.</p>
picture processing; pattern recognition; image feature description; finite prolate spheroidal sequences; frequency-domain locality; optimal feature sets; quadtree concept; image processing; feature extraction; texture description; image segmentation; scale invariance; multiresolution techniques; frequency-domain analysis; pattern recognition; picture processing; trees (mathematics)

M. Span and R. Wilson, "Finite Prolate Spheroidal Sequences and their Applications II: Image Feature Description and Segmentation," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 10, no. , pp. 193-203, 1988.
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