基于貝葉斯模型的骨架裁剪方法
doi: 10.11999/JEIT150003
基金項目:
國家自然科學基金青年科學基金(61100113),國家教育部留學歸國基金教外司留 [2012]940號,重慶市首批青年骨干教師項目(渝教人(2011)31號),重慶市基礎與前沿研究計劃項目(cstc2013jcyjA40062),重慶郵電大學學科引進人才基金(A2010-12)和重慶市研究生科研創(chuàng)新項目(CYS14142)
Approach of Skeleton Pruning with Bayesian Model
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摘要: 針對大部分骨架計算方法對輪廓噪聲的極端敏感性問題,該文提出一種基于貝葉斯模型的骨架裁剪方法。該方法利用貝葉斯理論對骨架及其生長過程進行建模,進而通過對模型的迭代優(yōu)化實現(xiàn)骨架候選分支的篩選裁剪。由于已有的重建誤差率在分析骨架時不能很好地體現(xiàn)骨架簡潔程度,故該文在骨架重建誤差率的基礎上綜合考慮骨架簡潔度,提出骨架有效率的概念來對骨架做客觀定量分析。實驗結果表明該文算法對輪廓噪聲具有較好的魯棒性,且裁剪出的骨架相比現(xiàn)有算法得到的骨架結構更加簡單,對形狀描述更加準確。Abstract: Considering the problem that most of the existing skeleton calculation methods exhibit extreme sensitivity to the shape noise, a Bayes based algorithm for the skeleton pruning is proposed . The algorithm models the skeleton and growth process with Bayesian statistics framework. Based on the model, an iterative optimization is performed to prune the candidate branches. Due to the fact that the existing reconstruction error can not evaluate the simplicity of skeletons well, a new concept called Effective Rate is proposed to make quantitative analysis on the pruned skeleton with taking the simplicity into consideration. The experiments show that the proposed algorithm is robust to the shape noise and acts better in simplifying the skeleton structure and representing shape accurately.
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