Synopsis
Presents the first unified theory of image segmentation, written by the winners of the 1985 Pattern Recognition Society medal. Until now, image processing algorithms have always been beset by uncertainties, no one method proving completely satisfactory. Wilson and Spann tackle the problem of uncertainty head-on. They describe a new class of algorithms (based, in part, on quadtrees) and demonstrate their applications, including grey level and texture segmentation. These algorithms produce excellent results in a wide range of synthetic and natural data. Provides many examples of applications from medicine to remote sensing.
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Synopsis
This book provides a unique in-depth study of the computational problem of image segmentation with examples relevant to applications from medicine to remote sensing. The book tackles head-on the problem of uncertainty in image segmentation and describes a new class of algorithms, together with applications to gray level and textural segmentation.
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Image Segmentation and Uncertainty (Pattern Recognition & Image Processing)
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