[1]廖翠萃,李敏,梁久祯,等.数值求解优化问题在活动轮廓模型上的应用[J].智能系统学报编辑部,2015,10(6):886-892.[doi:10.11992/tis.201507037]
 LIAO Cuicui,LI Min,LIANG Jiuzhen,et al.Application of a numerical solution to the optimization problem in the active contour model[J].CAAI Transactions on Intelligent Systems,2015,10(6):886-892.[doi:10.11992/tis.201507037]
点击复制

数值求解优化问题在活动轮廓模型上的应用(/HTML)
分享到:

《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年6期
页码:
886-892
栏目:
出版日期:
2015-12-25

文章信息/Info

Title:
Application of a numerical solution to the optimization problem in the active contour model
作者:
廖翠萃1 李敏2 梁久祯2 廖祖华1
1. 江南大学理学院, 江苏无锡 214122;
2. 江南大学物联网工程学院, 江苏无锡 214122
Author(s):
LIAO Cuicui1 LI Min2 LIANG Jiuzhen2 LIAO Zuhua1
1. Department of Information and Computaion Science, College of Science, Jiangnan University, Wuxi 214122, China;
2. Institute of Intelligent Systems and Network Computing, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, Chi
关键词:
CV模型LBF模型Runge-Kutta方法数值求解优化图像分割
Keywords:
CV modelLBF modelRunge-Kutta methodnumerical optimization procedureimage segment
分类号:
TP391.41
DOI:
10.11992/tis.201507037
摘要:
针对活动轮廓模型图像分割过程中迭代次数多,分割速度慢的问题,提出一种高阶的数值求解方法。分析活动轮廓模型中基于全局信息的CV模型,以及基于局部信息的LBF模型,LIF模型。使用二阶、三阶Runge-Kutta方法,原始Euler方法对模型进行数值求解实验对比分析。并对LBF模型中平滑项系数,时间步长的选取进行讨论。通过对非同质图像、同质图像的实验结果分析表明,所采用的数值方法能够有效地提高数值收敛精度、减少迭代次数、计算效率高。对不同系数和时间步长,数值方法也能表现出较好的稳定性。
Abstract:
In this paper, we analyze numerical optimization procedures and propose high-order numerical methods to deal with the problems of slow convergence and low efficiency in the active contour model. First, we analyze the global information region-based active contour Chan-Vese(CV) model, the local information region-based local binary fitting(LBF) model, and the local image fitting(LIF) model. Then, we compare and analyze image segment results utilizing second-and third-order explicit Runge-Kutta methods, and the standard explicit Euler method. We also analyze the segment results of different sliding coefficient parameters and time steps of the LBF model. The experimental results for the intensity inhomogeneities and common images show that the proposed numerical methods can reduce the number of iterations, and improve convergence accuracy and computational efficiency. In addition, for different coefficients and time steps, the proposed methods yield greater stability.

参考文献/References:

[1] VESE L A, CHAN T F. A multiphase level set framework for image segmentation using the Mumford and Shah model[J]. International Journal of Computer Vision, 2002, 50(3):271-293.
[2] CHAN T F, VESE L A. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2):266-277.
[3] COHEN L D, COHEN I. Finite-element methods for active contour models and balloons for 2-D and 3-D images[J]. IEEE Transactionson on Pattern Analysis Machine Intelligence, 1993, 15(11):1131-1147.
[4] LI Chunming, KAO C Y, GORE J C, et al. Implicit active contours driven by local binary fitting energy[J]. IEEE Conference on Computer Vision and Pattern Recognition, 2007:1-7.
[5] 潘改, 高立群, 张萍. 基于LBF方法的测地线活动轮廓模型[J]. 模式识别与人工智能, 2013, 26(12):1179-1184. PAN Gai, GAO Liqun, ZHANG Ping. Geodesic active contour based on LBF method[J]. Pattern Recognition and Aitificial Intelligence, 2013, 26(12):1179-1184.
[6] ZHANG Kaihua, SONG Huihui, ZHANG Lei. Active contours driven by local image fitting energy[J]. Pattern Recognition, 2010, 43(4):1199-1206.
[7] 刘瑞娟, 何传江, 原野. 融合局部和全局图像信息的活动轮廓模型[J]. 计算机辅助设计与图形学报, 2012, 24(3):364-371. LIU Ruijuan, HE Chuanjiang, YUAN Ye. Active contours driven by local and global image fitting energy[J]. Journal of Computer-Aided Design & Computer Graphics, 2012, 24(3):364-371.
[8] WANG Xiaofeng, HUANG Deshuang, XU Huan. An efficient local Chan-Vese model for image segmentation[J]. Pattern Recognition, 2010, 43(3):603-618.
[9] BAR L, SAPIRO G. Generalized Newton-type methods for energy formulations in image processing[J]. SIAM Journal on Imaging Sciences, 2009, 2(2):508-531.
[10] SCHEUERMANN B, ROSENHAHN B. Analysis of numerical methods for level set based image segmentation[J]. Advances in Visual Computing, 2009, 5876:196-207.
[11] LI Chunming, KAO C Y, GORE J C, et al. Minimization of region-scalable fitting energy for image segmentation[J]. IEEE Transcations on Image Processing, 2008, 17(10):1940-1949.
[12] GE Feng, WANG Song, LIU Tiecheng. New benchmark for image segmentation evaluation[J]. Journal of Electronic Imaging, 2007, 16(3):1010-1016.
[13] 任守纲, 马超, 徐焕良. 基于改进主动轮廓模型的图像分割方法研究[J]. 计算机科学, 2013, 40(7):289-292, 296. REN Shougang, MA Chao, XU Huanliang. Improved skeleton extracton algorithm based active contour model research[J]. Computer Science, 2013, 40(7):289-292, 296.
[14] CORMEN T H, LEISER C E, RIVEST R L, et al. Introduction to Algorithms[M]. 3rd ed. Cambridge, Mass:MIT press, 2009:350-400.
[15] JOHNSON M L. Essential numerical computer methods[M]. Burlington, MA:Academic Press, 2010:230-275.

备注/Memo

备注/Memo:
收稿日期:2015-04-30;改回日期:。
基金项目:国家自然科学基金资助项目(11401259);中央高校基本科研业务费专项资金资助项目(jusrr11407).
作者简介:廖翠萃,女, 1983年生,博士,讲师。发表SCI检索论文4篇、CSCD论文4篇,主持国家自然科学基金项目1项。主要研究方向为保结构数值计算方法。李敏,女, 1990年生,硕士。发表CSCD论文一篇,主持江苏省研究生科研实践项目一项。主要研究方向数字图像处理。梁久祯,男, 1968生,教授,博士。主要研究方向为机器视觉、图像处理等。在专业杂志与国内外会议等发表学术论文120余篇,其中被SCI检索8篇、EI检索33篇、CSCD检索60篇。主持国家自然科学基金项目1项、省部级项目3项。
通讯作者:廖翠萃.E-mail:cliao@jiangnan.edu.cn.
更新日期/Last Update: 1900-01-01