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Article

Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning

1
College of Mathematics and Computer Science, Hunan Normal University, Changsha 410081, China
2
School of Information Science and Engineering, Central South University, Changsha 410083, China
3
School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China
4
School of Electrical and Electronic Engineering, East China Jiaotong University, Nanchang 330013, China
*
Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Sensors 2016, 16(7), 998; https://doi.org/10.3390/s16070998
Received: 20 April 2016 / Revised: 14 June 2016 / Accepted: 23 June 2016 / Published: 29 June 2016
(This article belongs to the Special Issue Sensors for Agriculture)
The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images’ spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines. View Full-Text
Keywords: online product quality inspection; image spatial structure; sequential fragmentation theory; image statistical modeling; Weibull distribution; ensemble learning; semi-supervised learning online product quality inspection; image spatial structure; sequential fragmentation theory; image statistical modeling; Weibull distribution; ensemble learning; semi-supervised learning
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MDPI and ACS Style

Liu, J.; Tang, Z.; Xu, P.; Liu, W.; Zhang, J.; Zhu, J. Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning. Sensors 2016, 16, 998. https://doi.org/10.3390/s16070998

AMA Style

Liu J, Tang Z, Xu P, Liu W, Zhang J, Zhu J. Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning. Sensors. 2016; 16(7):998. https://doi.org/10.3390/s16070998

Chicago/Turabian Style

Liu, Jinping, Zhaohui Tang, Pengfei Xu, Wenzhong Liu, Jin Zhang, and Jianyong Zhu. 2016. "Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning" Sensors 16, no. 7: 998. https://doi.org/10.3390/s16070998

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