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A Novel High Recognition Rate Defect Inspection Method for Carbon Fiber Plain-Woven Prepreg Based on Image Texture Feature Compression. Polymers (Basel) 2022; 14:polym14091855. [PMID: 35567024 PMCID: PMC9103920 DOI: 10.3390/polym14091855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 02/05/2023] Open
Abstract
Carbon fiber plain-woven prepreg is one of the basic materials in the field of composite material design and manufacturing, in which defect identification is an important and easily neglected part of testing. Here, a novel high recognition rate inspection method for carbon fiber plain-woven prepregs is proposed for inspecting bubble and wrinkle defects based on image texture feature compression. The proposed method attempts to divide the image into non-overlapping block lattices as texture primitives and compress them into a binary feature matrix. Texture features are extracted using a gray level co-occurrence matrix. The defect types are further defined according to texture features by k-means clustering. The performance is evaluated in some existing computer vision and machine learning methods based on fiber recognition. By comparing the result, an overall recognition rate of 0.944 is achieved, which is competitive with the state-of-the-arts.
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Liu A, Yang E, Wu J, Teng Y, Yu L. Double sparse low rank decomposition for irregular printed fabric defect detection. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.11.078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Improved faster R-CNN for fabric defect detection based on Gabor filter with Genetic Algorithm optimization. COMPUT IND 2022. [DOI: 10.1016/j.compind.2021.103551] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Wei B, Hao K, Gao L, Tang XS. Bioinspired Visual-Integrated Model for Multilabel Classification of Textile Defect Images. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.2977974] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Cao J. Computer public course teaching based on improved machine learning and neural network algorithm. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
With the continuous progress of the times, the development of college education is also constantly tending to enrich and diversify. In the course of curriculum setting in many colleges and universities, more and more attention is paid to the teaching of computer courses for college students. In the course of setting up and teaching, we still follow the traditional teaching mode and do not pay more attention to students’ practical practice. By observing the computer course materials selected by many colleges and universities for students, we can see that most of the textbooks still focus on arranging some exercises from the point of view that science and engineering are students, and lack the basic knowledge of the curriculum. Because the understanding and research of computer course is not deep enough, the teaching effect obtained since the course is not ideal. By studying the relevant knowledge of machine learning and some important problems in the development of neural network algorithm theory, this paper puts forward some viewpoints based on the current curriculum system in colleges and universities in order to improve the learning quality of computer courses. And hope to build a variant learning model to improve students’ interest in computer courses. The exploration and inference of some knowledge in this paper are mostly my own views, some places are not professional enough, the majority of experts and scholars can criticize and correct at will.
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Affiliation(s)
- Jingxin Cao
- School of Computer, Xi’an Aeronautical University, Xi’an, China
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Zhang X, Yang Y, Li T, Zhang Y, Wang H, Fujita H. CMC: A consensus multi-view clustering model for predicting Alzheimer's disease progression. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 199:105895. [PMID: 33341477 DOI: 10.1016/j.cmpb.2020.105895] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 11/29/2020] [Indexed: 06/12/2023]
Abstract
Machine learning has been used in the past for the auxiliary diagnosis of Alzheimer's Disease (AD). However, most existing technologies only explore single-view data, require manual parameter setting and focus on two-class (i.e., dementia or not) classification problems. Unlike single-view data, multi-view data provide more powerful feature representation capability. Learning with multi-view data is referred to as multi-view learning, which has received certain attention in recent years. In this paper, we propose a new multi-view clustering model called Consensus Multi-view Clustering (CMC) based on nonnegative matrix factorization for predicting the multiple stages of AD progression. The proposed CMC performs multi-view learning idea to fully capture data features with limited medical images, approaches similarity relations between different entities, addresses the shortcoming from multi-view fusion that requires manual setting parameters, and further acquires a consensus representation containing shared features and complementary knowledge of multiple view data. It not only can improve the predication performance of AD, but also can screen and classify the symptoms of different AD's phases. Experimental results using data with twelve views constructed by brain Magnetic Resonance Imaging (MRI) database from Alzheimer's Disease Neuroimaging Initiative expound and prove the effectiveness of the proposed model.
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Affiliation(s)
- Xiaobo Zhang
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China; Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Yan Yang
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China; Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China.
| | - Tianrui Li
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China; Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Yiling Zhang
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China; Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Hao Wang
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China; Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Hamido Fujita
- Faculty of Software and Information Science, Iwate Prefectural University, Iwate, Japan
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Xu X, Chen J, Zhang H, Ng WW. D4Net: De-deformation defect detection network for non-rigid products with large patterns. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.05.050] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Shi B, Liang J, Di L, Chen C, Hou Z. Fabric defect detection via low-rank decomposition with gradient information and structured graph algorithm. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.08.100] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Wei B, Hao K, Gao L, Tang XS. Detecting textile micro-defects: A novel and efficient method based on visual gain mechanism. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.06.035] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Zhang J, Wang H, Tian Y, Liu K. An accurate fuzzy measure-based detection method for various types of defects on strip steel surfaces. COMPUT IND 2020. [DOI: 10.1016/j.compind.2020.103231] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Wei B, Hao K, Gao L, Tang XS, Zhao Y. A biologically inspired visual integrated model for image classification. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.04.081] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Wei B, He H, Hao K, Gao L, Tang XS. Visual interaction networks: A novel bio-inspired computational model for image classification. Neural Netw 2020; 130:100-110. [PMID: 32652433 DOI: 10.1016/j.neunet.2020.06.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 05/12/2020] [Accepted: 06/22/2020] [Indexed: 10/24/2022]
Abstract
Inspired by biological mechanisms and structures in neuroscience, many biologically inspired visual computational models have been presented to provide new solutions for visual recognition task. For example, convolutional neural network (CNN) was proposed according to the hierarchical structure of biological vision, which could achieve superior performance in large-scale image classification. In this paper, we propose a new framework called visual interaction networks (VIN-Net), which is inspired by visual interaction mechanisms. More specifically, self-interaction, mutual-interaction, multi-interaction, and adaptive interaction are proposed in VIN-Net, forming the first interactive completeness of the visual interaction model. To further enhance the representation ability of visual features, the adaptive adjustment mechanism is integrated into the VIN-Net model. Finally, our model is evaluated on three benchmark datasets and two self-built textile defect datasets. The experimental results demonstrate that the proposed model exhibits its efficiency on visual classification tasks. Furthermore, a textile industrial application shows that the proposed architecture outperforms the state-of-the-art approaches in classification performance.
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Affiliation(s)
- Bing Wei
- Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, Donghua University, Shanghai 201620, China; College of Information Sciences and Technology, Donghua University, Shanghai 201620, China
| | - Haibo He
- Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA.
| | - Kuangrong Hao
- Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, Donghua University, Shanghai 201620, China; College of Information Sciences and Technology, Donghua University, Shanghai 201620, China
| | - Lei Gao
- Business School, Shandong Normal University, Ji'nan 250014, China; Commonwealth Scientific and Industrial Research Organization (CSIRO), SA 5064, Australia
| | - Xue-Song Tang
- Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, Donghua University, Shanghai 201620, China; College of Information Sciences and Technology, Donghua University, Shanghai 201620, China
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Jia L, Chen C, Xu S, Shen J. Fabric defect inspection based on lattice segmentation and template statistics. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.10.032] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Liu J, Wang C, Su H, Du B, Tao D. Multistage GAN for Fabric Defect Detection. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 29:3388-3400. [PMID: 31870985 DOI: 10.1109/tip.2019.2959741] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Fabric defect detection is an intriguing but challenging topic. Many methods have been proposed for fabric defect detection, but these methods are still suboptimal due to the complex diversity of both fabric textures and defects. In this paper, we propose a generative adversarial network (GAN)-based framework for fabric defect detection. Considering existing challenges in real-world applications, the proposed fabric defect detection system is capable of learning existing fabric defect samples and automatically adapting to different fabric textures during different application periods. Specifically, we customize a deep semantic segmentation network for fabric defect detection that can detect different defect types. Furthermore, we attempted to train a multistage GAN to synthesize reasonable defects in new defect-free samples. First, a texture-conditioned GAN is trained to explore the conditional distribution of defects given different texture backgrounds. Given a novel fabric, we aim to generate reasonable defective patches. Then, a GAN-based fusion network fuses the generated defects to specific locations. Finally, the well-trained multistage GAN continuously updates the existing fabric defect datasets and contributes to the fine-tuning of the semantic segmentation network to better detect defects under different conditions. Comprehensive experiments on various representative fabric samples are conducted to verify the detection performance of our proposed method.
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A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9112173] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification.
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Jia L, Chen C, Liang J, Hou Z. Fabric defect inspection based on lattice segmentation and Gabor filtering. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.01.039] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Susan S, Sharma M. Automatic texture defect detection using Gaussian mixture entropy modeling. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.02.021] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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