Abstract
Skeletonization on binary image is a process for reducing foreground regions to a skeletal remnant, which largely preserves the original region’s connectivity. An algorithm for skeletonization based on k-nearest-neighbors is proposed in this paper. Instead of the fixed 8-neighborhood approach, which is the most common in the thinning field, our algorithm implements skeletonization based on k-nearest-neighbors. The method mainly consists of two stages: raw skeleton extraction and a novel thinning for post-processing. Extensive experiments are conducted and results show that the skeleton extracted by our method is precise, clean, and much smoother than the previous works.
Supported by the Chongqing Research Program of Basic Research and Frontier Technology (Grant No. cstc2019jcyj-msxmX0033), National Key R&D Program of China (Grant No. 2019YFD1100501) and National Natural Science Foundation of China (Grant No. 61701051).
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Ren, Y., Zhang, M., Zhou, H., Liu, J. (2022). Skeletonization Based on K-Nearest-Neighbors on Binary Image. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_21
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