一种新的图像分割算法

doi:10.3969/j.issn.1001-2400.2011.01.002

J4 ›› 2011, Vol. 38 ›› Issue (1): 8-15.doi: 10.3969/j.issn.1001-2400.2011.01.002

一种新的图像分割算法

徐建军;高山;毕笃彦;陈游

(空军工程大学工程学院，陕西西安 710038)

收稿日期:2010-07-07 出版日期:2011-02-20 发布日期:2011-04-08
通讯作者: 徐建军
作者简介:徐建军(1978-)，男，空军工程大学博士研究生，E-mail: xujianjunfly@126.com．
基金资助:
重点实验室基金资助项目(9140c610301080c6106)

A novel image segmentation algorithm

XU Jianjun;GAO Shan;BI Duyan;CHEN You

(School of Engineering, Air Force Engineering Univ., Xi'an 710038， China)

Received:2010-07-07 Online:2011-02-20 Published:2011-04-08
Contact: XU Jianjun

摘要/Abstract

摘要：

针对一类普遍存在的图像，采用具有生物学背景的交叉视觉皮质模型进行图像分割．将交叉视觉皮质模型所具有的符合人眼对亮度响应非线性要求的指数衰减的阈值机制，改进为适合图像分割处理的线性衰减的阈值机制，提出了线性阈值-交叉视觉皮质模型．同时采用改进的二维Tsallis交叉熵作为分割准则，可自动地确定交叉视觉皮质模型神经元的分割阈值以及循环迭代次数．实验表明，这种分割算法优于经典的OSTU算法和K-means算法，同时基于改进的二维Tsallis交叉熵准则优于基于二维最大Shannon熵准则、传统二维Tsallis交叉熵准则和一维最小Tsallis交叉熵准则．

关键词: 图像分割, 交叉视觉皮质模型, 二维Tsallis交叉熵, 线性阈值

Abstract:

The Intersecting Cortical Model (ICM) is adopted for image segmentation. Because the nonlinear exponential attenuation threshold mechanism is suitable for brightness respondence but not suitable for image segmentation, we change it to the linear threshold mechanism which leads to the LT-ICM. Meanwhile, an improved 2-D Tsallis cross-entropy segmentation rule is proposed for determining the segmentation threshold and iteration time automatically. Experiments show that the proposed algorithm is better than OSTU and K-means algorithms for the texture image segmentation. Besides, our rule is more effective than the 2-D max-Shannon-entropy rule, traditional 2-D Tsallis cross-entropy rule, and min-cross-entropy rule.

Key words: image segmentation, intersecting cortical model, 2-D Tsallis cross-entropy, linear-threshold

徐建军;高山;毕笃彦;陈游. 一种新的图像分割算法[J]. J4, 2011, 38(1): 8-15.

XU Jianjun;GAO Shan;BI Duyan;CHEN You. A novel image segmentation algorithm[J]. J4, 2011, 38(1): 8-15.

参考文献

[1] 万波, 王泉, 高有行. 图像分割的误差分散半调算法[J]. 西安电子科技大学学报, 2009, 36(3): 496-501.
Wan Bo, Wang Quan, Gao Youxing. Error Diffusion Halftone Algorithm Based on Image Segmentation[J]. Journal of Xidian University, 2009, 36(3): 496-501.
[2] 汪凯斌, 俞卞章, 王琦. 无须反复初始化的活动围道纹理分割方法[J]. 西安电子科技大学学报, 2008, 35(3): 542-545.
Wang Kaibin, Yu Bianzhang, Wang Qi. Texture Image Segmentation Using the Without Re-initialization Geodesic Active Contour Mode[J]. Journal of Xidian University, 2008, 35(3): 542-545.
[3] Ostu N. A Threshold Selection Method from Gray-level Histograms[J]. IEEE Trans on System, Man and Cybernetics, 1979,9(1):62-66.
[4] Abutaleb A S. Aytomatic Thresholding of Gray-level Picture Using 2-D Entropies[J]. Pattern Recognition, 1989(47):22-32.
[5] Ekblad U, Kinser J M. Theoretical Foundation of the Intersecting Cortical Model and Its Use for Detection of Aircrafts, Cars and Nuclear Explosion Tests [J]. Signal Processing, 2004, 84(7):1131-1146.
[6] Ekblad U, Kinser J M. The Intersecting Cortical Model in Image Processing [J]. Nuclear Instruments ＆ Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2004, 525(1):392-396.
[7] Waxman A M. Color Night Vision Fusion of Intensified Visible and Thermal IR Imagery [C]//Synthetic Vision for Vehicle Guidance Control. Orlando: SPIE, 1995:58-68.
[8] Zhao Wei. A Pixel-level Multisensor Image Fusion Algorithm Based on False Color[J]. Acta Electronia Sinica, 2003, 31(3): 368-371.
[9] Abutaleb A S. Automatic Thresholding of Gray-level Pictures Using 2D Entropy [J]. Computer Vision, Graphics, and Image Processing, 1989, 47(1):22-32.
[10] Brink A D. Threshold of Digital Images Using 2D Entropies [J]. Pattern Recognition, 1992, 25(8):803-808.
[11] 吴一全，张金矿. 基于改进的二维最大熵及粒子群递推的图像分割[J]. 计算机辅助设计与图形学学报,2008,10(20):1338-1344.
Wu Yiquan, Zhang Jinkuang. Thresholding Based on Improved 2D Maximum Entropy Method and Particle Swarm Optimization[J]. Journal of Computer-Aided Design＆Computer Graphics, 2008,10(20):1338-1344.
[12] 唐英干, 邸秋艳, 关新平,等. 基于最小Tsallis交叉熵的图像阈值分割方法[J].仪器仪表学报,2008, 29(9):1868-1871.
Tang Yinggan, Di Qiuyan, Guan Xinping, et al. Method for Thresholding Image Segmentation Based on Minimum Tsallis-cross Entropy[J].Chinese Journal of Scientific Instrument, 2008, 29(9):1868-1871.
[13] 唐英干, 邸秋艳, 赵立兴,等. 基于二维最小Tsallis交叉熵的图像阈值分割方法[J]. 物理学报, 2009(1): 17-23.
Tang Yinggan, Di Qiuyan, Zhao Lixing, et al. Image Thresholding Segmentation Based on Two-dimensional Minimum Tsallis-cross Entropy[J].Acta Physica Sinica, 2009(1): 17-23.
[14] Hler R A. Segmentation System Based on Thresholding[J]. Computer Vision, Graphics and Image Processing, 1981, 15(6):319-324.
[15] Li C H, Lee C K. Minimum Cross Entropy Threshold [J]. Pattern Recognition Letters, 1993, 26(4):617-625.

一种新的图像分割算法

A novel image segmentation algorithm

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	刘志强,张铭津,池源,李云松. 一种深度学习的硬件木马检测算法[J]. 西安电子科技大学学报, 2019, 46(6): 37-45.
[2]	吴成茂;上官若愚. 嵌入隐马尔科夫随机场的中智模糊聚类算法[J]. 西安电子科技大学学报, 2017, 44(6): 103-108+121.
[3]	汪云飞;毕笃彦;刘华伟;刘凌;赵晓林. 一种局部受限的规则聚类超像素算法[J]. 西安电子科技大学学报, 2016, 43(3): 95-100.
[4]	甘露;吴艳;王凡. 利用TMF和置信传播的无监督SAR图像分割[J]. J4, 2015, 42(6): 49-55.
[5]	张泽均;水鹏朗. G⁰分布中链码网格SAR图像分割算法[J]. J4, 2015, 42(5): 48-54.
[6]	王卫卫;杨塨鹏;吕畅;杨艳琦. 一种新的水平集图像分割模型[J]. J4, 2013, 40(6): 39-45+91.
[7]	张文娟;冯象初. 非凸低秩稀疏约束的图像超像素分割方法[J]. J4, 2013, 40(5): 86-91.
[8]	高山;王星;程嗣怡;李成;车飞. 交叉视觉皮质模型中向心自动波的实现[J]. J4, 2013, 40(1): 169-176.
[9]	汪云飞;毕笃彦;黄飞. 一种用于图像分割的等周算法改进[J]. J4, 2012, 39(2): 87-94+212.
[10]	马苗;梁建慧;郭敏. 基于细菌觅食算法的SAR图像阈值分割[J]. J4, 2011, 38(6): 152-158+178.
[11]	崔艳鹏；胡建伟；杨绍全；朱燕. 利用DT-GrowCut的MSTAR SAR图像自动分割技术[J]. J4, 2011, 38(3): 150-154.
[12]	刘建磊;冯大政. 一种全局最优的非匀质图像分割算法[J]. J4, 2011, 38(2): 66-71.
[13]	刘汉强;焦李成;赵凤. 基于局部相似性测度的SAR图像多层分割[J]. J4, 2010, 37(6): 1071-1076.
[14]	刘勍;许录平;马义德;苏哲;王勇. 一种超模糊熵ULPCNN图像自动分割新方法[J]. J4, 2010, 37(5): 817-824.
[15]	吕雁;冯大政. 融合Ratio边缘信息的水平集SAR图像分割方法[J]. J4, 2010, 37(3): 492-495.