International Journal of Computational Intelligence Systems

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Volume 9, Issue 1, January 2016, Pages 120 - 132

Toward Automated Quality Classification via Statistical Modeling of Grain Images for Rice Processing Monitoring

Authors
Jinping Liu*, 1, Zhaohui Tang2, Qing Chen2, Pengfei Xu1, Wenzhong Liu3, Jianyong Zhu4
1College of Mathematics and Computer Science, Hunan Normal University, Changsha, Hunan 410081, China
2School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China
3School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
4School of Electrical and Electronic Engineering, East China Jiaotong University, Nanchang, Jiangxi 330013, China
*corresponding author, Email:ljp202518@163.com
Corresponding Author
Jinping Liu
Received 4 June 2015, Accepted 7 December 2015, Available Online 1 January 2016.
DOI
10.1080/18756891.2016.1144158How to use a DOI?
Keywords
Process monitoring; Sequential fragmentation theory; Weibull distribution; Least squares-support vector machine (LS-SVM)
Abstract

Computer vision-based rice quality inspection has recently attracted increasing interest in both academic and industrial communities because it is a low-cost tool for fast, non-contact, nondestructive, accurate and objective process monitoring. However, current computer-vision system is far from effective in intelligent perception of complex grainy images, comprised of a large number of local homogeneous particles or fragmentations without obvious foreground and background. We introduce a well known statistical modeling theory of size distribution in comminution processes, sequential fragmentation theory, for the visual analysis of the spatial structure of the complex grainy images. A kind of omnidirectional multi-scale Gaussian derivative filter-based image statistical modeling method is presented to attain omnidirectional structural features of grain images under different observation scales. A modified LS-SVM classifier is subsequently established to automatically identify the processing rice quality. Extensive confirmative and comparative tests indicate the effectiveness and outperformance of the proposed method.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 1
Pages
120 - 132
Publication Date
2016/01/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1144158How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Jinping Liu
AU  - Zhaohui Tang
AU  - Qing Chen
AU  - Pengfei Xu
AU  - Wenzhong Liu
AU  - Jianyong Zhu
PY  - 2016
DA  - 2016/01/01
TI  - Toward Automated Quality Classification via Statistical Modeling of Grain Images for Rice Processing Monitoring
JO  - International Journal of Computational Intelligence Systems
SP  - 120
EP  - 132
VL  - 9
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1144158
DO  - 10.1080/18756891.2016.1144158
ID  - Liu2016
ER  -