A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes
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Title
A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes
Authors
Keywords
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Journal
TEXTILE RESEARCH JOURNAL
Volume -, Issue -, Pages 004051751881365
Publisher
SAGE Publications
Online
2018-12-02
DOI
10.1177/0040517518813656
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- Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation
- (2017) Guo-Sen Xie et al. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
- Hyperspectral image reconstruction by deep convolutional neural network for classification
- (2017) Yunsong Li et al. PATTERN RECOGNITION
- WITHDRAWN: Fast compressive tracking with robust example selection based on multiple instance learning in smart and autonomous systems
- (2017) Min Jia et al. PATTERN RECOGNITION
- RODEO: Robust DE-aliasing autoencOder for real-time medical image reconstruction
- (2017) Janki Mehta et al. PATTERN RECOGNITION
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- (2016) Rakesh Mehta et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
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- (2016) Zinelabidine Boulkenafet et al. IEEE Transactions on Information Forensics and Security
- Magnetic Resonance Fingerprinting with compressed sensing and distance metric learning
- (2016) Zhe Wang et al. NEUROCOMPUTING
- Differential evolution-based optimal Gabor filter model for fabric inspection
- (2016) Le Tong et al. NEUROCOMPUTING
- Color texture classification of yarn-dyed woven fabric based on dual-side scanning and co-occurrence matrix
- (2016) Binjie Xin et al. TEXTILE RESEARCH JOURNAL
- Characterization and assessment of fabric smoothness appearance based on sparse coding
- (2016) Pinghua Xu et al. TEXTILE RESEARCH JOURNAL
- Modelling Australian land use competition and ecosystem services with food price feedbacks at high spatial resolution
- (2015) Jeffery D. Connor et al. ENVIRONMENTAL MODELLING & SOFTWARE
- Kernel Fukunaga–Koontz Transform Subspaces for Classification of Hyperspectral Images With Small Sample Sizes
- (2015) Hamidullah Binol et al. IEEE Geoscience and Remote Sensing Letters
- A Nonlinear Multiple Feature Learning Classifier for Hyperspectral Images With Limited Training Samples
- (2015) Jiayi Li et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Fabric defect inspection using prior knowledge guided least squares regression
- (2015) Junjie Cao et al. MULTIMEDIA TOOLS AND APPLICATIONS
- Real-time and robust object tracking in video via low-rank coherency analysis in feature space
- (2015) Chenglizhao Chen et al. PATTERN RECOGNITION
- Feature Extraction Using Attraction Points for Classification of Hyperspectral Images in a Small Sample Size Situation
- (2014) Maryam Imani et al. IEEE Geoscience and Remote Sensing Letters
- Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples
- (2014) Maryam Imani et al. IEEE Geoscience and Remote Sensing Letters
- Texture Modeling Using Contourlets and Finite Mixtures of Generalized Gaussian Distributions and Applications
- (2014) Mohand Said Allili et al. IEEE TRANSACTIONS ON MULTIMEDIA
- A thermal-based defect classification method in textile fabrics with K-nearest neighbor algorithm
- (2014) Kazım Yıldız et al. Journal of Industrial Textiles
- A compressed sensing approach for efficient ensemble learning
- (2014) Lin Li et al. PATTERN RECOGNITION
- Deterministic Construction of Real-Valued Ternary Sensing Matrices Using Optical Orthogonal Codes
- (2013) Nam Yul Yu et al. IEEE SIGNAL PROCESSING LETTERS
- MT – BCS-Based Microwave Imaging Approach Through Minimum-Norm Current Expansion
- (2013) Lorenzo Poli et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- Interferer Identification in HetNets using Compressive Sensing Framework
- (2013) Niranjan M Gowda et al. IEEE TRANSACTIONS ON COMMUNICATIONS
- Yarn-dyed woven defect characterization and classification using combined features and support vector machine
- (2013) Wenyu Li et al. JOURNAL OF THE TEXTILE INSTITUTE
- A new method for classification of woven structure for yarn-dyed fabric
- (2013) Dejun Zheng et al. TEXTILE RESEARCH JOURNAL
- Progressive Compressed Sensing and Reconstruction of Multidimensional Signals Using Hybrid Transform/Prediction Sparsity Model
- (2012) G. Coluccia et al. IEEE Journal on Emerging and Selected Topics in Circuits and Systems
- Knitted fabric defect classification for uncertain labels based on Dempster–Shafer theory of evidence
- (2010) Mahdi Tabassian et al. EXPERT SYSTEMS WITH APPLICATIONS
- Compressed Sensing and Redundant Dictionaries
- (2008) Holger Rauhut et al. IEEE TRANSACTIONS ON INFORMATION THEORY
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