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• W2076341009 abstract "Skeletonization provides a compact yet effective representation of 2-D and 3-D objects, which is useful in many low- and high-level image-related tasks including object representation, retrieval, manipulation, matching, registration, tracking, recognition, and compression. Also, it facilitates efficient characterization of topology, geometry, scale, and other related local properties of an object. Despite that the notion of skeletonization is well defined in a continuous space, in the discrete world of image processing and computer vision, it is not, and, therefore, it is more often described using procedural approaches. Several computational approaches are available in the literature for extracting the skeleton of an object, some of which are widely different even at the level of their basic principles. In this chapter, we present a comprehensive and concise survey of different skeletonization principles and algorithms and discuss their properties, challenges, and benefits. Different important aspects of skeletonization, namely, topology preservation, skeleton simplification and pruning, multiscale skeletonization, and parallelization are discussed. Finally, various applications of skeletonization are reviewed, and the fundamental issues related to the analysis of performance of different skeletonization algorithms are debated." @default.
• W2076341009 created "2016-06-24" @default.
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• W2076341009 date "1992-01-01" @default.
• W2076341009 modified "2023-10-18" @default.
• W2076341009 title "Performance characterization in image analysis: thinning, a case in point" @default.
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• W2076341009 doi "https://doi.org/10.1016/0167-8655(92)90108-c" @default.
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