基于貝葉斯模型的骨架裁剪方法

秦紅星; 孫穎

doi:10.11999/JEIT150003

基于貝葉斯模型的骨架裁剪方法

doi: 10.11999/JEIT150003

秦紅星¹,
孫穎¹

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)

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出版歷程
- 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: 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。
- 骨架 /
- 形狀 /
- 裁剪 /
- 貝葉斯 /
- 幾何處理 /
- 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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參考文獻(22)

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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