@Article{csse.2018.33.115,
AUTHOR = {Wenyu Qu, Zhiyang Li, Junfeng Wu, Yinan Wu, Zhaobin Liu},
TITLE = {Effective Piecewise Linear Skeletonization of Sparse Shapes},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {33},
YEAR = {2018},
NUMBER = {2},
PAGES = {115--123},
URL = {http://www.techscience.com/csse/v33n2/39963},
ISSN = {},
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 is applied. Finally, a re nement step is proposed to further improve the
quality of the skeleton and deal with noise or different local shape scales. Numerous experiments demonstrate that our algorithm can effectively handle
several disconnected shapes in an image simultaneously, and generate more faithful skeletons for shapes with intersections or different local scales than
classic methods.},
DOI = {10.32604/csse.2018.33.115}
}