›› 2012, Vol. 42 ›› Issue (05): 1267-1272.

Previous Articles     Next Articles

Intelligent counting system of soft fibrils collection based on machine vision

XING Xiao-xue1,2, LIU Fu1, MA Dong-mei3, ZHAI Wei-wei4, WANG Fang-rong1   

  1. 1. College of Communications Engineering, Jilin University, Changchun 130022, China;
    2. College of Information Engineering, Changchun University, Changchun 130022,China;
    3. Changchun Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Changchun 130022, China;
    4. College of Physics, Jilin University, Changchun 130022, China
  • Received:2011-12-14 Online:2012-09-01 Published:2012-09-01

PDF (PC)

428

Abstract: Based on machine vision, an intelligent counting system on soft fibrils collection was designed. The hemispherical LED source was used in the system, and the fiber slice was manufactured. The fiber slice was imaged by a microscope optical system, recorded by a CCD camera, and transferred by the image grabber into a computer. Then the quantities of the soft fibrils collection were calculated by the image processing system, and whether the numbers were qualified or not could be judged. A novel image processing method was proposed. The original images obtained in different light intensity could be fused based on maximum region entropy. The optimal threshold of the fusion image could be got based on SOFM neural networks. Based on the above threshold, the binary images of the originals could be refused. After that the quantities of the soft fibrils collection could be counted through boundary separation and counting algorithms based on statistical value. The experimental results show that the detection error of the system is less than 1%, the maximum standard deviation is no more than 0.07 and the automatic and intelligent counting function on the soft fibrils collection can be accomplished.

Key words: computer application, maximum region entropy, SOFM, image fusion, soft fibrils collection

CLC Number: 

  • TP391.4
[1] Snezana B Stankovic. Static lateral compression of hemp/filament hybrid yarn knitted fabrics[J]. Fibers and Polymers, 2008,9(2): 187-193.
[2] 李星华,王伟霞.对单丝不足粘胶长丝的排查方法[J].人造纤维,2007, 37 (5) : 34-35. Li Xing-hua, Wang Wei-xia. Method of viscose filament detection[J]. Artificial Fiber, 2007, 37(5): 34 -35.
[3] Alok Nath Roy, Gautam Basu. Development of newer products with spun wrapped Jute yarns[J]. Indian Journal of Natural Products and Resources. 2010, 1 (1) : 11-16.
[4] Nansen Christian, Herrman Timothy, Swanson Rand. Machine vision detection of bonemeal in animal feed samples[J]. Applied Spectroscopy, 2010, 64(6): 637-643.
[5] Sergio Cubero, Nuria Aleixos, Enrique Moltó. Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables[J]. Food Bioprocess Technol, 2011, 4: 487-504.
[6] 于杰,肖国强,代毅.粘连蚕卵计数方法的研究[J].计算机工程与应用, 2011, 47(8): 179-182. Yu Jie, Xiao Guo-qiang, Dai Yi. Study on counting algorithm for overlapping silkworm ovum image[J]. Computer Engineering and Applications, 2011, 47 (8): 179-182.
[7] 孟芳兵. 一种基于最大区域熵值的图像融合方法[J]. 武汉理工大学学报:信息与管理工程版,2009,31(1):19-21. Meng Fang-bing. An image fusion technique based on the maximum region entropy[J]. Joural of Wuhan University of Technology(Information &Management Engineering), 2009, 31(1):19-21.
[8] Zhang Jia-quan, Feng Yi, Lin Xiao-long, et al.Segmentation of viscose filament fracture surface image based on SOFM network fusion//The 2nd International Conference on Information Engineering and Computer Science, 2010: 1778-1780.
[9] 张彬,郑永果,马芳,等.医学图像融合算法的应用与研究[J]. 重庆医科大学学报, 2011, 36(2):188-191. Zhang Bin, Zheng Yong-guo, Ma Fang, et al. Research and application of medical image fusion algorithm[J]. Journal of Chongqing Medical University, 2011,36(2):188-191.
[10] 张二虎,梁鹏飞. 基于SOFM的灰度图像彩色化算法[J].计算机工程, 2011,37(4):227-229. Zhang Er-hu, Liang Peng-fei. Colorization algorithm for grayscale images based on SOFM[J]. Computer Engineering, 2011,37(4):227-229.
[11] 邵建斌,陈刚.基于分水岭算法的气泡图像分割[J].西安理工大学学报,2011,27(2):185-189. Shao Jian-bin, Chen Gang. Image segmentation of bubble based on watershed algorithm[J]. Journal of Xi'an University of Technology,2011,27(2):185-189.
[12] 武媛媛,岳晓奎.基于分水岭算法的空间目标图像分割方法[J].计算机仿真,2011,28(2):300-303. Wu Yuan-yuan,Yue Xiao-kui. Image segmentation for space target based on watershed algorithm[J]. Computer Simulation, 2011, 28(2): 300-303.
[13] 梁旭东,武妍. 基于邻域特征与聚类的图像分割方法[J].计算机工程, 2011,37(2):201-203. Liang Xu-dong, Wu Yan. Image segmentation method based on neighborhood feature and clustering[J]. Computer Engineering, 2011, 37 (2) : 201 -203.
[14] 张健,宋刚.基于分裂式K均值聚类的图像分割方法[J].计算机应用,2011,31(2):372-374. Zhang Jian, Song Gang. Image segmentation by fissive K-mean clustering method[J]. Journal of Computer Applications, 2011, 31 (2) : 372-374.
[15] Swagatam Das, Sudeshna Sil. Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm[J]. Information Sciences, 2010, 180(8): 1237-1256.
[1] LIU Fu,ZONG Yu-xuan,KANG Bing,ZHANG Yi-meng,LIN Cai-xia,ZHAO Hong-wei. Dorsal hand vein recognition system based on optimized texture features [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1844-1850.
[2] WANG Li-min,LIU Yang,SUN Ming-hui,LI Mei-hui. Ensemble of unrestricted K-dependence Bayesian classifiers based on Markov blanket [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1851-1858.
[3] JIN Shun-fu,WANG Bao-shuai,HAO Shan-shan,JIA Xiao-guang,HUO Zhan-qiang. Synchronous sleeping based energy saving strategy of reservation virtual machines in cloud data centers and its performance research [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1859-1866.
[4] ZHAO Dong,SUN Ming-yu,ZHU Jin-long,YU Fan-hua,LIU Guang-jie,CHEN Hui-ling. Improved moth-flame optimization method based on combination of particle swarm optimization and simplex method [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1867-1872.
[5] LIU En-ze,WU Wen-fu. Agricultural surface multiple feature decision fusion disease judgment algorithm based on machine vision [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1873-1878.
[6] OUYANG Dan-tong, FAN Qi. Clause-level context-aware open information extraction [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1563-1570.
[7] LIU Fu, LAN Xu-teng, HOU Tao, KANG Bing, LIU Yun, LIN Cai-xia. Metagenomic clustering method based on k-mer frequency optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1593-1599.
[8] GUI Chun, HUANG Wang-xing. Network clustering method based on improved label propagation algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1600-1605.
[9] LIU Yuan-ning, LIU Shuai, ZHU Xiao-dong, CHEN Yi-hao, ZHENG Shao-ge, SHEN Chun-zhuang. LOG operator and adaptive optimization Gabor filtering for iris recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1606-1613.
[10] LIU Zhe, XU Tao, SONG Yu-qing, XU Chun-yan. Image fusion technology based on NSCT and robust principal component analysis model with similar information [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1614-1620.
[11] CHE Xiang-jiu, WANG Li, GUO Xiao-xin. Improved boundary detection based on multi-scale cues fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1621-1628.
[12] ZHAO Hong-wei, LIU Yu-qi, DONG Li-yan, WANG Yu, LIU Pei. Dynamic route optimization algorithm based on hybrid in ITS [J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[13] HUANG Hui, FENG Xi-an, WEI Yan, XU Chi, CHEN Hui-ling. An intelligent system based on enhanced kernel extreme learning machine for choosing the second major [J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[14] FU Wen-bo, ZHANG Jie, CHEN Yong-le. Network topology discovery algorithm against routing spoofing attack in Internet of things [J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236.
[15] CAO Jie, SU Zhe, LI Xiao-xu. Image annotation method based on Corr-LDA model [J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd