融合马尔可夫随机场与量子粒子群聚类的棉花图像分割算法

doi:10.11963/1002-7807.ljhljh.20180126

PDF(2013 KB)

棉花学报 ›› 2018, Vol. 30 ›› Issue (2) : 197-204. DOI: 10.11963/1002-7807.ljhljh.20180126

研究简报

融合马尔可夫随机场与量子粒子群聚类的棉花图像分割算法

龙金辉^1,2，朱真峰¹

作者信息 +

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

Long Jinhui^1,2, Zhu Zhenfeng¹

Author information +

History +

摘要

【目的】提高采摘机器人棉花图像处理系统中的图像分割准确率。【方法】提出融合马尔可夫随机场与量子粒子群聚类的图像分割算法。步骤：将读入的RGB模式彩色图像转换成灰度图像；利用本算法分割图像；通过对连通区域面积阈值的设定获取目标区域。使用本算法分割不同角度采集的棉花正面图像与棉花侧面图像，并采用分割精度、峰值信噪比，分别与Otsu算法、模糊聚类图像分割算法、量子粒子群图像分割算法和马尔可夫随机场图像分割算法进行效果比较。【结果】本算法分割精度、峰值信噪比平均值分别为98.94%、77.48 dB，与Otsu算法、模糊聚类图像分割算法、量子粒子群图像分割算法、马尔科夫随机场图像分割算法相比，分割精度、峰值信噪比分别提高2.47～4.56百分点、9.81～13.11 dB。【结论】本算法处理棉花图像具有更高的分割精度以及峰值信噪比。

Abstract

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.

导出引用

龙金辉, 朱真峰. 融合马尔可夫随机场与量子粒子群聚类的棉花图像分割算法[J]. 棉花学报, 2018, 30(2): 197-204. https://doi.org/10.11963/1002-7807.ljhljh.20180126

Long Jinhui, Zhu Zhenfeng. Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster[J]. Cotton Science, 2018, 30(2): 197-204. https://doi.org/10.11963/1002-7807.ljhljh.20180126

参考文献

1] Huang L W, He D J, Yang S X. Segmentation on ripe Fuji apple with fuzzy 2D entropy based on 2D histogram and GA optimization[J］. Intelligent Automation & Soft Computing, 2013, 19(3): 239-251.
[2] 吕继东, 赵德安, 姬伟. 苹果采摘机器人目标果实快速跟踪识别方法[J］. 农业机械学报, 2014, 45(1): 65-72.
Lü Jidong, Zhao De’an, Ji Wei. Fast tracing recognition method of target fruit for apple harvesting robot[J］. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(1): 65-72.
[3] 王丹丹, 徐越, 宋怀波, 等. 融合K-means与Ncut算法的无遮挡双重叠苹果目标分割与重建[J］. 农业工程学报, 2015,31(10): 227-234.
Wang Dandan, Xu Yue, Song Huaibo, et al. Fusion of K-means and Ncut algorithm to realize segmentation and reconstruction of two overlapped apples without blocking by branches and leaves[J］. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(10): 227-234.
[4] 徐黎明, 吕继东. 基于同态滤波和K均值聚类算法的杨梅图像分割[J］. 农业工程学报, 2015, 31(14): 202-208.
Xu Liming, Lü Jidong. Bayberry image segmentation based on homomorphic filtering and K-means clustering algorithm[J］. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(14): 202-208.
[5] Harrell R C, Slaughter D C, Adsit P D. A fruit tracking system for robotic harvesting[J］. Machine Vision and Applications, 1989, 2(2): 69-80.
[6] Grift T, Zhang Q, Kondo N, et al. A review of automation and robotics for the bio-industry[J］. Journal of Biomechatronics Engineering, 2008, 1(1): 37-54.
[7] 蔡健荣, 周小军, 李玉良, 等. 基于机器视觉自然场景下成熟柑橘识别[J］. 农业工程学报, 2008, 24(1): 175-178.
Cai Jianrong, Zhou Xiaojun, Li Yuliang, et al. Recognition of mature oranges in natural scene based on machine vision[J］. Transactions of the Chinese Society of Agricultural Engineering, 2008, 24(1): 175-178.
[8] Ghabousian A, Shamsi M. Segmentation of apple color images utilizing fuzzy clustering algorithms[J］. Advances in Digital Multimedia, 2012, 1(1): 59-63.
[9] Moradi G, Shamsi M, Sedaghi M H, et al. Fruit defect detection from color images using ACM and MFCM algorithms[C］// Proceedings of the International Conference on Electronic Devices, Systems and Application, April 25-27, 2011. Kuala Lumpur, Malaysia: Institute of Electrical and Electronics Engineers, 2011: 182-186.
[10] 赵海波, 周向红. 基于计算机视觉的番茄催熟与正常熟识别[J］. 农业工程学报, 2011, 27(2): 355-359.
Zhao Haibo, Zhou Xianghong. Recognition of artificial ripening tomato and nature mature tomato based on computer vision[J］. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(2): 355-359.
[11] 熊俊涛, 邹湘军, 陈丽娟, 等. 基于机器视觉的自然环境中成熟荔枝识别[J］. 农业机械学报, 2011, 42(9): 162-166.
Xiong Juntao, Zou Xiangjun, Chen Lijuan, et al. Recognition of mature litchi in natural environment based on machine vision [J］. Transactions of the Chinese Society for Agricultural Machinery, 2011, 42(9): 162-166.
[12] 熊俊涛, 邹湘军, 陈丽娟, 等. 荔枝采摘机械手果实识别与定位技术[J］. 江苏大学学报, 2012, 33(1): 1-5.
Xiong Juntao, Zou Xiangjun, Chen Lijuan, et al. Fruit recognition and positioning technology of litchi picking manipulator[J］. Journal of Jiangsu University, 2012, 33(1): 1-5.
[13] 陈科尹, 邹湘军, 熊俊涛, 等. 基于视觉显著性改进的水果图像模糊聚类分割算法[J］. 农业工程学报, 2013, 29(6): 157-165.
Chen Keyin, Zou Xiangjun, Xiong Juntao, et al. Improved fruit fuzzy clustering image segmentation algorithm based on visual saliency[J］. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(6): 157-165.
[14] 王富春, 徐越, 宋怀波. 西红柿果实目标识别方法研究[J］. 农机化研究, 2015(10): 24-28.
Wang Fuchun, Xu Yue, Song Huaibo. Study on identification of tomatos[J］. Journal of Agricultural Mechanization Research, 2015(10): 24-28.
[15] 刘广瑞, 黄真, 毛树春, 等. OTSU最佳阈值法在棉花幼苗识别中的应用研究[J］. 棉花学报, 2013, 25(1): 86-89.
Liu Guangrui, Huang Zhen, Mao Shuchun, et al. Application study about optimal threshold method in cotton seedling recognition[J］. Cotton Science, 2013, 25(1): 86-89.
[16] 张成梁, 李蕾, 董全成, 等. 基于颜色和形状特征的机采棉杂质识别方法[J］. 农业机械学报, 2016, 47(7): 28-34.
Zhang Chengliang, Li Lei, Dong Quancheng, et al. Recognition method for machine-harvested cotton impurities based on color and shape features[J］. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(7): 28-34.
[17] 王玲, 王萍, 陈兵林, 等. 基于颜色阈值的田间籽棉图像分割技术[J］. 作物学报, 2010, 36(3): 503-507.
Wang Ling, Wang Ping, Chen Binlin, et al. Image segmentation technique of field cotton based on color threshold[J］. Acta Agronomica Sinica, 2010, 36(3): 503-507.
[18] 刘坤, 费树岷, 汪木兰, 等. 基于改进随机Hough变换的棉桃识别技术[J］. 农业机械学报, 2010, 41(8):160-165.
Liu Kun, Fei Shumin, Wang Mulan, et al. Cotton recognition based on randomized Hough transform[J］. Transactions of the Chinese Society for Agricultural Machinery, 2010, 41(8): 160-165.
[19] 刘金帅, 赖惠成, 贾振红. 基于YCbCr颜色空间和Fisher判别分析的棉花图像分割研究[J］. 作物学报, 2011, 37(7): 1274-1279.
Liu Jinshuai, Lai Huicheng, Jia Zhenhong. Image segmentation of cotton based on YCbCr color space and fisher discrimination analysis[J］. Acta Agronomica Sinica, 2011, 37(7): 1274-1279.
[20] 韦皆顶, 费树岷, 汪木兰, 等. 基于HSV颜色模型的自然场景下棉花图像分割策略研究[J］. 棉花学报, 2008, 20(1): 34-38.
Wei Jieding, Fei Shumin, Wang Mulan, et al. Research on the segmentation strategy of the cotton images on the natural condition based upon the HSV color-space model[J］. Cotton Science, 2008, 20(1): 34-38.
[21] 陈钦政, 赖惠成, 王星, 等. 一种基于支持向量机的棉花图像分割算法[J］. 计算机工程, 2013, 39(5): 266-269.
Chen Qinzhen, Lai Huicheng, Wang Xing, et al. An cotton image segmentation algorithm based on support vector machine[J］. Computer Engineering, 2013, 39(5): 266-269.
[22] 刘广瑞, 黄真, 毛树春, 等. OTSU最佳阈值法在棉花幼苗识别中的应用研究[J］. 棉花学报, 2013, 25(1): 86-89.
Liu Guangrui, Huang Zhen, Mao Shuchun, et al. Application study about OTSU optimal threshold method in cotton seedling recognition[J］. Cotton Science, 2013, 25(1): 86-89.
[23] Hafiane A, Zavidovique B. FCM with spatial and multiresolution constraints for image segmentation[C］ // Proceedings of the Second International Conference on Image Analysis and Recognition. Toronto, Canada: Institute of Electrical and Electronics Engineers, 2005: 17-23.
[24] Sun J, Feng B, Xu W B. Particle swarm optimization with particles having quantum behavior[C］// Proceeding of the 2004 Congress on Evolutionary Computation. Portland, USA: Institute of Electrical and Electronics Engineers, 2004: 325-331.
[25] Bezdek J C. Pattern recognition with fuzzy objection function algorithms[M]. New York: Plenum Press, 1981.
[26] 张小红, 宁红梅. 基于混沌粒子群和模糊聚类的图像分割算法[J］. 计算机应用研究, 2011, 28(12): 4786-4789.
Zhang Xiaohong, Ning Hongmei. Fuzzy clustering image segmentation algorithm based on CPSO[J］. Application Research of Computers, 2011, 28(12): 4786-4789
[27] 许亚骏, 吴小俊. 基于量子行为粒子群优化的软子空间聚类算法[J］. 模式识别与人工智能, 2016, 29(6): 558-565.
Xu Yajun, Wu Xiaojun. Soft subspace clustering algorithm based on quantum-behaved particle swarm optimization[J］. Pattern Recognition and Artificial Intelligence, 2016, 29(6): 558-565.
[28] 佘黎煌, 钟华, 张石. 结合马尔可夫随机场与C-模糊均值聚类的脑图像分割[J］. 中国图象图形学报, 2012, 17(12): 1554-1560.
She Lihuang, Zhong Hua, Zhang Shi. Fuzzy C-means clustering algorithm combined with Markov random field for brain MR image segmentation[J］. Journal of Image and Graphics, 2012, 17(12): 1554-1560.

基金

国家自然科学基金(61170223)；国家自然科学基金联合基金(U1204610)

PDF(2013 KB)

311

Accesses

Citation

Detail

段落导航

摘要
Abstract
关键词
Keywords
引用本文
参考文献
基金

Received	Published
2017-05-10	2018-03-15
Issue Date
2018-03-15

选择文件类型/文献管理软件名称

选择包含的内容

摘要

Abstract

关键词

Keywords

引用本文

{{custom_sec.title}}

{{custom_sec.title}}

参考文献

{{custom_fnGroup.title_cn}}

脚注

基金

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

摘要

Abstract

关键词

Keywords

引用本文

{{custom_sec.title}}

{{custom_sec.title}}

参考文献

{{custom_fnGroup.title_cn}}

脚注

基金