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• W4381337060 abstract "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." @default.
• W4381337060 created "2023-06-21" @default.
• W4381337060 creator A5019385536 @default.
• W4381337060 creator A5041754730 @default.
• W4381337060 date "2023-06-19" @default.
• W4381337060 modified "2023-10-14" @default.
• W4381337060 title "Edge-Supervised Linear Object Skeletonization for High-Speed Camera" @default.
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• W4381337060 doi "https://doi.org/10.3390/s23125721" @default.
• W4381337060 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37420888" @default.
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