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《Computer Integrated Manufacturing Systems》 2011-05
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Point cloud denoising algorithm based on swarm intelligent

LI Jin-jiang1,2,ZHANG Cai-ming1,FAN Hui2(1.School of Computer Science and Technology,Shandong University,Jinan 250101,China;2.School of Computer Science and Technology,Shandong Institute of Economic & Technology,Yantai 264005,China)  
To avoid phenomena of excessive smoothing and the local distortion,a kernel-function-based ant colony clustering algorithm was proposed to analyze the point clouds data,which was linear in high-dimensional feature space.Curvature and normal were mapped into feature space by kernel function,using weighted distance as the similarity measure to analyze the possible noise points and local features.Interclass variance was adopted to calculate the threshold adaptively when smoothing the normal vector.Experimental results showed that the presented algorithm had significant improvements than the classical algorithms which preserved some feature information of original data.
【Fund】: 国家自然科学基金资助项目(60970105);; 山东省自然科学基金资助项目(2009ZRB01620 ZR2009GQ005);; 山东省教育厅科技计划资助项目(J08LJ06)~~
【CateGory Index】: TN911.4
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