Twodimensional Cross Entropy Thresholding Based on Chaotic Resilient Particle Swarm Optimization or Decomposition

Abstract

Abstract: A twodimensional cross entropy image thresholding method based on chaotic resilient particle swarm optimization (CRPSO) or decomposition was proposed. Firstly, chaotic resilient particle swarm optimization was used to find the optimal threshold of twodimensional cross entropy method. The recursive algorithm was adopted to avoid the repetitive computation of fitness function in iterative procedure. As a result, the computing speed was improved greatly. Then, the computation of twodimensional cross entropy method was converted into two onedimensional spaces, which made the computation complexity further reduce from O(L2) to O(L). The experimental results show that, the two methods proposed in this paper can greatly reduce the running time while the segmented result is as good as or better than the existing twodimensional cross entropy thresholding method.

Key words: image segmentation, threshold selection, twodimensional cross entropy, chaotic resilient particle swarm optimization（CRPSO）, decomposition

CLC Number:

TN 911.73

WU Yi-Quan, ZHANG Xiao-Jie, WU Shi-Hua. Twodimensional Cross Entropy Thresholding Based on Chaotic Resilient Particle Swarm Optimization or Decomposition[J]. Journal of Shanghai Jiaotong University, 2011, 45(03): 301-0307.

References

［1］吴一全, 朱兆达. 图像处理中阈值选取方法30年(1962—1992)的进展(一)［J］. 数据采集与处理, 1993, 8(3): 193201.

WU Yiquan, ZHU Zhaoda. 30 years (1962—1992) of the developments in threshold selection methods in image processing (1)［J］. Journal of Data Acquisition & Processing, 1993, 8(3): 193201.

［2］吴一全, 朱兆达. 图像处理中阈值选取方法30年(1962—1992)的进展(二)［J］. 数据采集与处理, 1993, 8(4): 268282.

WU Yiquan, ZHU Zhaoda. 30 years (1962—1992) of the developments in threshold selection methods in image processing (2)［J］. Journal of Data Acquisition & Processing, 1993, 8(4): 268282.

［3］Sezgin M, Sankur B. Survey over image thresholding techniques and quantitative performance evaluation［J］. Journal of Electronic Imaging, 2004, 13(1): 145165.［4］Bardera A, Boada I, Feixas M, et al. Image segmentation using excess entropy ［J］. Journal of Signal Processing Systems, 2009, 54(13): 205214.

［5］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, 20(10): 13381344.

WU Yiquan, ZHANG Jinkuang. Thresholding based on improved 2D maximum entropy method and particle swarm optimization［J］. Journal of ComputerAided Design & Computer Graphics, 2008, 20(10): 13381344.

［6］Li C H, Lee C K. Minimum cross entropy thresholding［J］. Pattern Recognition, 1993, 26(4): 617625.

［7］Brink A D, Pendock N E. Minimum crossentropy threshold selection［J］. Pattern Recognition, 1996, 29(1): 179189.

［8］Pal N R, On minimum crossentropy thresholding［J］. Pattern Recognition, 1996, 29(4): 575580.

［9］Li C H, Tam P K S. An iterative algorithm for minimum cross entropy thresholding［J］. Pattern Recognition Letters, 1998, 19(8): 771776.

［10］雷博, 范久伦. 灰度图像的二维交叉熵阈值分割法［J］. 光子学报, 2009, 38(6): 15721576.

LEI Bo, FAN Jiulun. Twodimensional crossentropy thresholding segmentation method for graylevel images［J］. Acta Photonica Sinica, 2009, 38(6): 15721576.

［11］高雷阜, 刘旭旺. 基于混沌的弹性粒子群全局优化算法［J］. 控制与决策, 2009, 24(10): 15451548.

GAO Leifu, LIU Xuwang. Resilient partical swarm global optimization algorithm based on chaos［J］. Control and Decision, 2009, 24(10): 15451548.

［12］岳峰, 左旺孟, 王宽全. 基于分解的灰度图像二维阈值选取算法［J］. 自动化学报, 2009, 35(7): 10221027.

YUE Feng, ZUO Wangmeng, WANG Kuanquan. Decomposition based twodimensional threshold algorithm for gray images［J］. Acta Automatica Sinica, 2009, 35(7): 10221027.

[1]	ZHANG Suhuan (张素焕), ZHANG Junfeng∗ (张俊锋), LI Shuo (李烁), YU Shanen (余善恩). Event-Triggered Control of Positive Semi-Markovian Jump Systems Without/with Input Saturation [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(5): 723-736.
[2]	JIANG Zhiguo (蒋志国), CHANG Qing∗ (常青). USSL Net: Focusing on Structural Similarity with Light U-Structure for Stroke Lesion Segmentation [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 485-497.
[3]	QIU Jiekai, DING Zhaowei, SONG Chunyu, CHEN Longzhu. Horizontal Dynamic Response of Pile Based on Layered Generalized Gibson Foundation [J]. Journal of Shanghai Jiao Tong University, 2022, 56(4): 431-442.
[4]	ZHAO Yong, SU Dan. Rogue Wave Prediction Based on Four Combined Long Short-Term Memory Neural Network Models [J]. Journal of Shanghai Jiao Tong University, 2022, 56(4): 516-522.
[5]	ZHANG Yue (张月), LIU Shijie (刘世界), LI Chunlai (李春来), WANG Jianyu (王建宇). Application of Deep Learning Method on Ischemic Stroke Lesion Segmentation [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(1): 99-111.
[6]	SUN Chong, TIAN Tian, ZHU Xiaocheng, DU Zhaohui. Analysis of POD and EPOD for Unsteady Flow Field of Wind Turbine Airfoil [J]. Journal of Shanghai Jiao Tong University, 2022, 56(1): 45-52.
[7]	GUO Zhiyuan, YU Peixiang, OUYANG Hua. Shear Layer Instability of Flow Around a Circular Cylinder Based on Large Eddy Simulation [J]. Journal of Shanghai Jiao Tong University, 2021, 55(8): 924-933.
[8]	ZHOU Junjie, YU Jianbo. Online Measurement of Machining Tool Wear Based on Machine Vision [J]. Journal of Shanghai Jiao Tong University, 2021, 55(6): 741-749.
[9]	XU Jiangchang (许江长), HE Shamin (何莎敏), YU Dedong (于德栋), WU Yiqun (吴轶群), CHEN Xiaojun, (陈晓军). Automatic Segmentation Method for Cone-Beam Computed Tomography Image of the Bone Graft Region within Maxillary Sinus Based on the Atrous Spatial Pyramid Convolution Network [J]. J Shanghai Jiaotong Univ Sci, 2021, 26(3): 298-305.
[10]	ZHENG Yiyang, NI He, JIN Jiashan. An Operation Stability Assessment Method of a Single-Parameter in team Power System Based on MSOP [J]. Journal of Shanghai Jiao Tong University, 2021, 55(11): 1438-1444.
[11]	ZHUO Pengcheng, YAN Jin, ZHENG Meimei, XIA Tangbin, XI Lifeng. GA-OIHF Elman Neural Network Algorithm for Fault Diagnosis of Full Life Cycle of Rolling Bearing [J]. Journal of Shanghai Jiao Tong University, 2021, 55(10): 1255-1262.
[12]	LI Hongguang, GUO Ying, SUI Ping, CAI Bin, SU Linghua. Two-Dimensional DOA Estimation for Multi-Hopping Signals Based on Sparse Bayes [J]. Journal of Shanghai Jiao Tong University, 2020, 54(4): 359-368.
[13]	LI Chunxiang, ZHANG Haoyi. Hybridizing Multivariate Empirical Mode Decomposition and Extreme Learning Machine to Predict Non-Stationary Processes [J]. Journal of Shanghai Jiao Tong University, 2020, 54(4): 376-386.
[14]	XIE Hairun,WU Yadong,OUYANG Hua,WANG Anzheng. Modal Analysis of Wake Induced Vibration Transient Process Based on POD and DMD [J]. Journal of Shanghai Jiaotong University, 2020, 54(2): 176-185.
[15]	ZHANG Xiaohui,BAI Junli,GU Xiechong,MA Ning. An Explicit Parallel Successive Over-Relaxation Method for Simulation of 2-Dimensional Incompressible Flows [J]. Journal of Shanghai Jiaotong University, 2019, 53(6): 681-687.