基于贝叶斯模型的骨架裁剪方法

秦红星; 孙颖

doi:10.11999/JEIT150003

基于贝叶斯模型的骨架裁剪方法

doi: 10.11999/JEIT150003

秦红星¹,
孙颖¹

基金项目:

国家自然科学基金青年科学基金(61100113)，国家教育部留学归国基金教外司留 [2012]940号，重庆市首批青年骨干教师项目(渝教人(2011)31号)，重庆市基础与前沿研究计划项目(cstc2013jcyjA40062)，重庆邮电大学学科引进人才基金(A2010-12)和重庆市研究生科研创新项目(CYS14142)

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出版历程
- 收稿日期: 2015-01-05
- 修回日期: 2015-05-13
- 刊出日期: 2015-09-19

Approach of Skeleton Pruning with Bayesian Model

Qin Hong-xing¹,
Sun Ying¹

摘要

摘要: 针对大部分骨架计算方法对轮廓噪声的极端敏感性问题，该文提出一种基于贝叶斯模型的骨架裁剪方法。该方法利用贝叶斯理论对骨架及其生长过程进行建模，进而通过对模型的迭代优化实现骨架候选分支的筛选裁剪。由于已有的重建误差率在分析骨架时不能很好地体现骨架简洁程度，故该文在骨架重建误差率的基础上综合考虑骨架简洁度，提出骨架有效率的概念来对骨架做客观定量分析。实验结果表明该文算法对轮廓噪声具有较好的鲁棒性，且裁剪出的骨架相比现有算法得到的骨架结构更加简单，对形状描述更加准确。
- 骨架 /
- 形状 /
- 裁剪 /
- 贝叶斯 /
- 几何处理 /
- 骨架有效率
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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Gong Y, Lazebnik S, Gordo A, et al.. Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(12): 2916-2929.

Kim V G, Chaudhuri S, Guibas L, et al.. Shape2pose: Human-centric shape analysis[J]. ACM Transactions on Graphics (TOG), 2014, 33(4): 70-79.

Huang S S, Shamir A, Shen C H, et al.. Qualitative organization of collections of shapes via quartet analysis[J]. ACM Transactions on Graphics (TOG), 2013, 32(4): 96-96.

Blum H. Biological shape and visual science (Part I)[J]. Journal of Theoretical Biology, 1973, 38(2): 205-287.

Xu J. A generalized morphological skeleton transform using both internal and external skeleton points[J]. Pattern Recognition, 2014, 47(8): 2607-2620.

Song Z, Yu J, Zhou C, et al.. Skeleton correspondence construction and its applications in animation style reusing[J]. Neurocomputing, 2013, 120(10): 461-468.

Al Nasr K, Liu C, Rwebangira M, et al.. Intensity-based skeletonization of cryoEM gray-scale images using a true segmentation-free algorithm[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 2013, 10(5): 1289-1298.

Mayya N and Rajan V T. Voronoi diagrams of polygons: A framework for shape representation[J]. Journal of Mathematical Imaging and Vision, 1996, 6(4): 355-378.

Daz-Pernil D, Pea-Cantillana F, and Gutirrez-Naranjo M A. Cellular Automata in Image Processing and Geometry[M]. Berlin: Springer International Publishing, 2014: 47-63.

Choi W P, Lam K M, and Siu W C. Extraction of the Euclidean skeleton based on a connectivity criterion[J]. Pattern Recognition, 2003, 36(3): 721-729.

Sobiecki A, Yasan H C, Jalba A C, et al.. Mathematical Morphology and Its Applications to Signal and Image Processing[M]. Berlin: Springer International Publishing, 2013: 425-439.

Babu G R M, Srikrishna A, Challa K, et al.. An error free compression algorithm using morphological decomposition[C]. 2012 International Conference on Recent Advances in Computing and Software Systems (RACSS), Chennai, 2012: 33-36.

Karimipour F and Ghandehari M. Transactions on Computational Science XX[M]. Berlin: Springer International Publishing, 2013: 138-157.

Jalba A C, Kustra J, and Telea A C. Surface and curve skeletonization of large 3D models on the GPU[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1495-1508.

Karimov A, Mistelbauer G, Schmidt J, et al.. ViviSection: skeleton-based volume editing[C]. Computer Graphics Forum, Leipzig, 2013, 32(3pt4): 461-470.

Cicconet M, Geiger D, Gunsalus K C, et al.. Mirror symmetry histograms for capturing geometric properties in images[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, 2014: 2981-2986.

Mokhtarian F and Mackworth A K. A theory of multiscale, curvature-based shape representation for planar curves[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(8): 789-805.

Ogniewicz R L and Kbler O. Hierarchic voronoi skeletons[J]. Pattern Recognition, 1995, 28(3): 343-359.

Couprie M, Coeurjolly D, and Zrour R. Discrete bisector function and Euclidean skeleton in 2D and 3D[J]. Image and Vision Computing, 2007, 25(10): 1543-1556.

Bai X, Latecki L J, and Liu W Y. Skeleton pruning by contour partitioning with discrete curve evolution[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(3): 449-462.

Sebastian T B, Klein P N, and Kimia B B. Recognition of shapes by editing their shock graphs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(5): 550-571.

Shen W, Bai X, Yang X W, et al.. Skeleton pruning as trade-off between skeleton simplicity and reconstruction error[J]. Science China Information Sciences, 2013, 56(4): 1-14.

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