Next Article in Journal
Molecularly Imprinted Polymer Nanoparticles for Formaldehyde Sensing with QCM
Next Article in Special Issue
Classification of Kiwifruit Grades Based on Fruit Shape Using a Single Camera
Previous Article in Journal
Microcantilever Displacement Measurement Using a Mechanically Modulated Optical Feedback Interferometer
Previous Article in Special Issue
Verification of Geometric Model-Based Plant Phenotyping Methods for Studies of Xerophytic Plants
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(7), 998; doi:10.3390/s16070998

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
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)
View Full-Text   |   Download PDF [5133 KB, uploaded 29 June 2016]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top