|Table of Contents|

[1] Yu Lu, Xie Jun, Wu Lenan, et al. Statistical segmentation model on lattices [J]. Journal of Southeast University (English Edition), 2008, 24 (1): 10-14. [doi:10.3969/j.issn.1003-7985.2008.01.003]
Copy

Statistical segmentation model on lattices()
格点上的一种统计分割模型
Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
24
Issue:
2008 1
Page:
10-14
Research Field:
Computer Science and Engineering
Publishing date:
2008-03-30

Info

Title:
Statistical segmentation model on lattices
格点上的一种统计分割模型
Author(s):
Yu Lu1;  2;  Xie Jun3;  Wu Lenan1
1School of Information Science and Engineering, Southeast University, Nanjing 210096, China
2 Institute of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China
3 Institute of Command Automation, PLA University of Science and Technology, Nanjing 210007, China
俞璐1;  2;  谢钧3;  吴乐南1
1东南大学信息科学与工程学院, 南京 210096; 2 解放军理工大学通信工程学院, 南京 210007; 3 解放军理工大学指挥自动化学院, 南京 210007
Keywords:
image segmentation;  curve evolution;  conditional entropy;  lattice;  labelling problem
图像分割;  曲线演化;  条件熵;  格点;  标号问题
PACS:
TP391
DOI:
10.3969/j.issn.1003-7985.2008.01.003
Abstract:
To reduce the difficulty of implementation and shorten the runtime of the traditional Kim-Fisher model, an entirely discrete Kim-Fisher-like model on lattices is proposed.The discrete model is directly built on the lattices, and the greedy algorithm is used in the implementation to continually decrease the energy function.First, regarding the gray values in images as discrete-valued random variables makes it possible to make a much simpler estimation of conditional entropy.Secondly, a uniform method within the level set framework for two-phase and multiphase segmentations without extension is presented.Finally, a more accurate approximation to the curve length on lattices with multi-labels is proposed.The experimental results show that, compared with the continuous Kim-Fisher model, the proposed model can obtain comparative results, while the implementation is much simpler and the runtime is dramatically reduced.
为了克服Kim-Fisher模型实现难度大、运行速度慢的问题, 提出了离散的近似Kim-Fisher模型.该离散模型的目标函数直接定义在格点上, 采用贪心法进行优化.首先, 把图像的灰度值视为离散的随机变量, 从而可以采用更为简单的方法估计条件熵.其次, 针对基于水平集技术的二区域和多区域图像分割, 提出一种无须扩展的统一的方法.最后, 还提出一种多标号格点上曲线长度的近似方法, 该方法比现有的方法更加准确.实验结果表明, 同传统的连续Kim-Fisher模型相比, 所提出的模型在取得相当的分割效果的同时, 简化了实现过程, 并大大降低了运行时间.

References:

[1] Kim J, Fisher J W, Yezzi A, et al.A nonparametric statistical method for image segmentation using information theory and curve evolution [J].IEEE Transactions on Image Processing, 2005, 14(10):1486-1502.
[2] Song B, Chan T.A fast algorithm for level set based optimization, CAM 02-68 [R].Los Angeles:University of California at Los Angeles, 2002.
[3] Shi Y, Karl W C.Real-time tracking using level sets [C]//Proc of IEEE International Conference on Computer Vision and Pattern Recognition.San Diego, USA, 2005:34-41.
[4] Gibou F, Fedkiw R.A fast level set based algorithm for segmentation [C]//Proc of the 4th Annual International Conference on Statistics and Mathematics.Hawaii, USA, 2005:281-291.
[5] Yezzi A, Kichenassamy A, Kumar A, et al.A geometric snake model for segmentation of medical imagery [J].IEEE Transactions on Medical Image, 1997, 16(2):199-209.
[6] Yu Lu, Wang Qiao, Wu Lenan, et al.Mumford-shah model with fast algorithm on lattice [C]//Proc of IEEE International Conference on Acoustics, Speech, and Signal Processing.Toulouse, France, 2006:681-684.
[7] Mumford D, Shah J.Optimal approximations by piecewise smooth functions and associated variational problems [J].Communications on Pure and Applied Mathematics, 1989, 42(5):577-685.

Memo

Memo:
Biographies: Yu Lu(1973—), female, doctor;Wu Lenan(corresponding author), male, doctor, professor, wuln@seu.edu.cn.
Citation: Yu Lu, Xie Jun, Wu Lenan.Statistical segmentation model on lattices[J].Journal of Southeast University(English Edition), 2008, 24(1):10-14.
Last Update: 2008-03-20