Fabric defect detection method based on cascaded low-rank decomposition
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Clothing Science and Technology"
DOI: 10.1108/ijcst-03-2019-0037
Abstract: The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.,This paper proposed a fabric defect… read more here.
Keywords: low rank; detection; defect detection; rank decomposition ... See more keywords
Fabric Defect Detection via Low-Rank Decomposition With Gradient Information
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DOI: 10.1109/access.2019.2939843
Abstract: Low-rank decomposition model has been widely used in fabric defect detection, where a matrix is decomposed into a low-rank matrix representing the defect-free region (background) of the image and a sparse matrix identifying the defective… read more here.
Keywords: rank decomposition; low rank; fabric defect; rank ... See more keywords
Pansharpening With Joint Local Low Rank Decomposition and Hierarchical Geometric Filtering
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DOI: 10.1109/access.2019.2940482
Abstract: Extracting matched details of the PANchromatic (PAN) image and injecting them into the MultiSpectral (MS) images, is very crucial in pansharpening. In this paper, a new pansharpening method based on Joint Local Low Rank Decomposition… read more here.
Keywords: rank decomposition; geometric filtering; low rank; local low ... See more keywords
Multi-Matrices Low-Rank Decomposition With Structural Smoothness for Image Denoising
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DOI: 10.1109/tcsvt.2019.2890880
Abstract: In this paper, we propose a multi-matrices low-rank decomposition method for image denoising. In this new method, the total variation (TV) norm is incorporated into low-rank approximation analysis to achieve structural smoothness and to improve… read more here.
Keywords: rank decomposition; low rank; rank; structural smoothness ... See more keywords
Holistic CNN Compression via Low-Rank Decomposition with Knowledge Transfer
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DOI: 10.1109/tpami.2018.2873305
Abstract: Convolutional neural networks (CNNs) have achieved remarkable success in various computer vision tasks, which are extremely powerful to deal with massive training data by using tens of millions of parameters. However, CNNs often cost significant… read more here.
Keywords: rank decomposition; compression; cnn compression; low rank ... See more keywords