Please wait a minute...
FITEE
Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (1): 54-61    DOI: 10.1631/jzus.C0910797
    
New separation algorithm for touching grain kernels based on contour segments and ellipse fitting
Lei Yan1,2, Cheol-Woo Park1, Sang-Ryong Lee1, Choon-Young Lee*,1
1 School of Mechanical Engineering, Kyungpook National University, Daegu 702-701, Korea 2 School of Technology, Beijing Forestry University, Beijing 100083, China
New separation algorithm for touching grain kernels based on contour segments and ellipse fitting
Lei Yan1,2, Cheol-Woo Park1, Sang-Ryong Lee1, Choon-Young Lee*,1
1 School of Mechanical Engineering, Kyungpook National University, Daegu 702-701, Korea 2 School of Technology, Beijing Forestry University, Beijing 100083, China
 全文: PDF(283 KB)  
摘要： A new separation algorithm based on contour segments and ellipse fitting is proposed to separate the ellipse-like touching grain kernels in digital images. The image is filtered and converted into a binary image first. Then the contour of touching grain kernels is extracted and divided into contour segments (CS) with the concave points on it. The next step is to merge the contour segments, which is the main contribution of this work. The distance measurement (DM) and deviation error measurement (DEM) are proposed to test whether the contour segments pertain to the same kernel or not. If they pass the measurement and judgment, they are merged as a new segment. Finally with these newly merged contour segments, the ellipses are fitted as the representative ellipses for touching kernels. To verify the proposed algorithm, six different kinds of Korean grains were tested. Experimental results showed that the proposed method is efficient and accurate for the separation of the touching grain kernels.
关键词： Separation algorithm;  Touching grains;  Contour segments;  Ellipse fitting    
Abstract: A new separation algorithm based on contour segments and ellipse fitting is proposed to separate the ellipse-like touching grain kernels in digital images. The image is filtered and converted into a binary image first. Then the contour of touching grain kernels is extracted and divided into contour segments (CS) with the concave points on it. The next step is to merge the contour segments, which is the main contribution of this work. The distance measurement (DM) and deviation error measurement (DEM) are proposed to test whether the contour segments pertain to the same kernel or not. If they pass the measurement and judgment, they are merged as a new segment. Finally with these newly merged contour segments, the ellipses are fitted as the representative ellipses for touching kernels. To verify the proposed algorithm, six different kinds of Korean grains were tested. Experimental results showed that the proposed method is efficient and accurate for the separation of the touching grain kernels.
Key words: Separation algorithm    Touching grains    Contour segments    Ellipse fitting
收稿日期: 2009-12-29 出版日期: 2010-01-10
CLC:  TP391  
基金资助: Project supported by the Grant of the Korean Ministry of Education, Science and Technology under the Regional Core Research Program
通讯作者: Choon-Young Lee     E-mail: cylee@knu.ac.kr
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Lei Yan
Cheol-Woo Park
Sang-Ryong Lee
Choon-Young Lee

引用本文:

Lei Yan, Cheol-Woo Park, Sang-Ryong Lee, Choon-Young Lee. New separation algorithm for touching grain kernels based on contour segments and ellipse fitting. Front. Inform. Technol. Electron. Eng., 2011, 12(1): 54-61.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C0910797    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I1/54

[1] Yuan-ping Nie, Yi Han, Jiu-ming Huang, Bo Jiao, Ai-ping Li. 基于注意机制编码解码模型的答案选择方法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 535-544.
[2] Rong-Feng Zhang , Ting Deng , Gui-Hong Wang , Jing-Lun Shi , Quan-Sheng Guan . 基于可靠特征点分配算法的鲁棒性跟踪框架[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 545-558.
[3] Gopi Ram , Durbadal Mandal , Sakti Prasad Ghoshal , Rajib Kar . 使用猫群算法优化线性天线阵列的最佳阵因子辐射方向图：电磁仿真验证[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 570-577.
[4] Lin-bo Qiao, Bo-feng Zhang, Jin-shu Su, Xi-cheng Lu. 结构化稀疏学习综述[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 445-463.
[5] . 一种基于描述逻辑的体系质量需求建模与验证方法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(3): 346-361.
[6] Ali Darvish Falehi, Ali Mosallanejad. 使用基于多目标粒子群算法多层自适应模糊推理系统晶闸管控制串联电容器补偿技术的互联多源电力系统动态稳定性增强器[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(3): 394-409.
[7] Wen-yan Xiao, Ming-wen Wang, Zhen Weng, Li-lin Zhang, Jia-li Zuo. 基于语料库的小学英语认识率及教材选词策略研究[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(3): 362-372.
[8] Li Weigang. 用于评估共同作者学术贡献的第一和其他合作者信用分配模式[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 180-194.
[9] Jun-hong Zhang, Yu Liu. 应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 272-286.
[10] Hui Chen, Bao-gang Wei, Yi-ming Li, Yong-huai Liu, Wen-hao Zhu. 一种易用的实体识别消歧系统评测框架[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 195-205.
[11] Yue-ting Zhuang, Fei Wu, Chun Chen, Yun-he Pan. 挑战与希望：AI2.0时代从大数据到知识[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 3-14.
[12] Bo-hu Li, Hui-yang Qu, Ting-yu Lin, Bao-cun Hou, Xiang Zhai, Guo-qiang Shi, Jun-hua Zhou, Chao Ruan. 基于综合集成研讨厅的群体智能设计研究[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 149-152.
[13] Le-kui Zhou, Si-liang Tang, Jun Xiao, Fei Wu, Yue-ting Zhuang. 基于众包标签数据深度学习的命名实体消歧算法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 97-106.
[14] Yong-hong Tian, Xi-lin Chen, Hong-kai Xiong, Hong-liang Li, Li-rong Dai, Jing Chen, Jun-liang Xing, Jing Chen, Xi-hong Wu, Wei-min Hu, Yu Hu, Tie-jun Huang, Wen Gao. AI2.0时代的类人与超人感知：研究综述与趋势展望[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 58-67.
[15] Yu-xin Peng, Wen-wu Zhu, Yao Zhao, Chang-sheng Xu, Qing-ming Huang, Han-qing Lu, Qing-hua Zheng, Tie-jun Huang, Wen Gao. 跨媒体分析与推理：研究进展与发展方向[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 44-57.