Parallel thinning and skeletonization algorithm based on cellular automaton

Zhang, Fan; Chen, Xiao-Pan; Zhang, Xinhong

doi:10.1007/s11042-020-09660-5

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…Several researchers have proposed improved skeletonization algorithms to address the limitations of existing algorithms. Lu et al, [ 26 ], improved skeletonization by preserving important structures, while Zhang et al [ 27 , 28 ], introduced fast parallel thinning algorithms. Lee et al [ 29 ] presented the three-dimensional parallel thinning method using an octree data structure.…”

Section: Related Workmentioning

confidence: 99%

Edge-Supervised Linear Object Skeletonization for High-Speed Camera

Wang

Yamakawa

2023

Sensors

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This paper presents a high-speed skeletonization algorithm for detecting the skeletons of linear objects from their binary images. The primary objective of our research is to achieve rapid extraction of the skeletons from binary images while maintaining accuracy for high-speed cameras. The proposed algorithm uses edge supervision and a branch detector to efficiently search inside the object, avoiding unnecessary computation on irrelevant pixels outside the object. Additionally, our algorithm addresses the challenge of self-intersections in linear objects with a branch detection module, which detects existing intersections and initializes new searches on emerging branches when necessary. Experiments on various binary images, such as numbers, ropes, and iron wires, demonstrated the reliability, accuracy, and efficiency of our approach. We compared the performance of our method with existing skeletonization techniques, showing its superiority in terms of speed, especially for larger image sizes.

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