Wenyu Qu1, Zhiyang Li2,*, Junfeng Wu2, Yinan Wu3, Zhaobin Liu2
Computer Systems Science and Engineering, Vol.33, No.2, pp. 115-123, 2018, DOI:10.32604/csse.2018.33.115
Abstract Conventional image skeletonization techniques implicitly assume the pixel level connectivity. However, noise inside the object regions destroys the
connectivity and exhibits sparseness in the image. We present a skeletonization algorithm designed for these kinds of sparse shapes. The skeletons are
produced quickly by using three operations. First, initial skeleton nodes are selected by farthest point sampling with circles containing the maximum effective
information. A skeleton graph of these nodes is imposed via inheriting the neighborhood of their associated pixels, followed by an edge collapse operation.
Then a skeleton tting process based on feature-preserving Laplacian smoothing More >
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