title: Visual Perception-Based Statistical Modeling of Complex Grain Image for Product Quality Monitoring and Supervision on Assembly Production Line.
abstract: Computer vision as a fast, low-cost, noncontact, and online monitoring technology has been an important tool to inspect product quality, particularly on a large-scale assembly production line. However, the current industrial vision system is far from satisfactory in the intelligent perception of complex grain images, comprising a large number of local homogeneous fragmentations or patches without distinct foreground and background. We attempt to solve this problem based on the statistical modeling of spatial structures of grain images. We present a physical explanation in advance to indicate that the spatial structures of the complex grain images are subject to a representative Weibull distribution according to the theory of sequential fragmentation, which is well known in the continued comminution of ore grinding. To delineate the spatial structure of the grain image, we present a method of multiscale and omnidirectional Gaussian derivative filtering. Then, a product quality classifier based on sparse multikernel-least squares support vector machine is proposed to solve the low-confidence classification problem of imbalanced data distribution. The proposed method is applied on the assembly line of a food-processing enterprise to classify (or identify) automatically the production quality of rice. The experiments on the real application case, compared with the commonly used methods, illustrate the validity of our method.
authors.affiliation: College of Mathematics and Computer Science, Hunan Normal University, Changsha, Hunan, China.|Key Laboratory of High Performance Computing and Stochastic Information Processing of Ministry of Education of China, Changsha, Hunan, China.|School of Automation, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Jinping
authors.initials: J
authors.lastname: Liu
authors.affiliation: School of Information Science and Engineering, Central South University,Changsha, Hunan, China.
authors.forename: Zhaohui
authors.initials: Z
authors.lastname: Tang
authors.affiliation: School of Information Science and Engineering, Central South University,Changsha, Hunan, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Zhang
authors.affiliation: School of Information Science and Engineering, Central South University,Changsha, Hunan, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Chen
authors.affiliation: College of Mathematics and Computer Science, Hunan Normal University, Changsha, Hunan, China.|Key Laboratory of High Performance Computing and Stochastic Information Processing of Ministry of Education of China, Changsha, Hunan, China.
authors.forename: Pengfei
authors.initials: P
authors.lastname: Xu
authors.affiliation: School of Automation, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Wenzhong
authors.initials: W
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D002523:Edible Grain / Q000033:anatomy & histology*; D005525:Food-Processing Industry / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D012275:Oryza / Q000033:anatomy & histology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011786:Quality Control; D060388:Support Vector Machine; D014796:Visual Perception