Computer-Based Electronic Engineering Technology

Abstract Most of the current image fusion algorithms directly process the original image, neglect the analysis of the main components of the image, and have a great influence on the effect of image fusion. In this paper, the main component analysis method is used to decompose the image, divided into low rank matrix and sparse matrix, introduced compression perception technology and NSST transformation algorithm to process the two types of matrix, according to the corresponding fusion rules to achieve image fusion, through experimental results: this algorithm has greater mutual information compared with traditional algorithms, structural information similarity and average gradient.

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Small Infrared Target Detection Based on Low-Rank and Sparse Matrix Decomposition

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.214 ◽

2012 ◽

Vol 239-240 ◽

pp. 214-218 ◽

Cited By ~ 2

Author(s):

Cheng Yong Zheng ◽

Hong Li

Keyword(s):

Target Detection ◽

Sparse Matrix ◽

Infrared Image ◽

Matrix Decomposition ◽

Low Rank ◽

Target Component ◽

Lagrange Method ◽

Rank Matrix ◽

Augmented Lagrange Method ◽

Low Rank Matrix

Sparse and low-rank matrix decomposition (SLMD) tries to decompose a matrix into a low-rank matrix and a sparse matrix, it has recently attached much research interest and has good applications in many fields. An infrared image with small target usually has slowly transitional background, it can be seen as the sum of low-rank background component and sparse target component. So by SLMD, the sparse target component can be separated from the infrared image and then be used for small infrared target detection (SITD). The augmented Lagrange method, which is currently the most efficient algorithm used for solving SLMD, was applied in this paper for SITD, some parameters were analyzed and adjusted for SITD. Experimental results show our algorithm is fast and reliable.

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Research on Background Modeling Based on Low-Rank Matrix Recovery

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.635-637.1056 ◽

2014 ◽

Vol 635-637 ◽

pp. 1056-1059 ◽

Cited By ~ 1

Author(s):

Bao Yan Wang ◽

Xin Gang Wang

Keyword(s):

Sparse Matrix ◽

Background Modeling ◽

Low Rank ◽

Complex Scene ◽

Matrix Recovery ◽

Rank Matrix ◽

Training Samples ◽

The Individual ◽

Low Rank Matrix Recovery ◽

Low Rank Matrix

Key and difficult points of background subtraction method lie in looking for an ideal background modeling under complex scene. Stacking the individual frames as columns of a big matrix, background parts can be viewed as a low-rank background matrix because of large similarity among individual frames, yet foreground parts can be viewed as a sparse matrix as foreground parts play a small role in individual frames. Thus the process of video background modeling is in fact a process of low-rank matrix recovery. Background modeling based on low-rank matrix recovery can separate foreground images from background at the same time without pre-training samples, besides, the approach is robust to illumination changes. However, there exist some shortcomings in background modeling based on low-rank matrix recovery by analyzing numerical experiments, which is developed from three aspects.

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Robust Principal Component Thermography for Defect Detection in Composites

Sensors ◽

10.3390/s21082682 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2682

Author(s):

Samira Ebrahimi ◽

Julien Fleuret ◽

Matthieu Klein ◽

Louis-Daniel Théroux ◽

Marc Georges ◽

...

Keyword(s):

Defect Detection ◽

Sparse Matrix ◽

Research Effort ◽

Principal Component ◽

Low Rank ◽

Computational Time ◽

Rank Matrix ◽

Detection Capabilities ◽

Signal Processing Methods ◽

Low Rank Matrix

Pulsed Thermography (PT) data are usually affected by noise and as such most of the research effort in the last few years has been directed towards the development of advanced signal processing methods to improve defect detection. Among the numerous techniques that have been proposed, principal component thermography (PCT)—based on principal component analysis (PCA)—is one of the most effective in terms of defect contrast enhancement and data compression. However, it is well-known that PCA can be significantly affected in the presence of corrupted data (e.g., noise and outliers). Robust PCA (RPCA) has been recently proposed as an alternative statistical method that handles noisy data more properly by decomposing the input data into a low-rank matrix and a sparse matrix. We propose to process PT data by RPCA instead of PCA in order to improve defect detectability. The performance of the resulting approach, Robust Principal Component Thermography (RPCT)—based on RPCA, was evaluated with respect to PCT—based on PCA, using a CFRP sample containing artificially produced defects. We compared results quantitatively based on two metrics, Contrast-to-Noise Ratio (CNR), for defect detection capabilities, and the Jaccard similarity coefficient, for defect segmentation potential. CNR results were on average 40% higher for RPCT than for PCT, and the Jaccard index was slightly higher for RPCT (0.7395) than for PCT (0.7010). In terms of computational time, however, PCT was 11.5 times faster than RPCT. Further investigations are needed to assess RPCT performance on a wider range of materials and to optimize computational time.

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An Efficient Missing Data Imputation Based On Co-Cluster Sparse Matrix Learning

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit195220 ◽

2019 ◽

pp. 215-222

Author(s):

F. Femila ◽

G. Sridevi ◽

D. Swathi ◽

K. Swetha

Keyword(s):

Missing Data ◽

Computational Models ◽

Genome Wide Association Study ◽

Sparse Matrix ◽

Low Rank ◽

Missing Data Imputation ◽

Rank Matrix ◽

Tensor Optimization ◽

Traditional Approaches ◽

Low Rank Matrix

Missing data padding is an important problem that is faced in real time. This makes the task of data processing challenging. This paper aims to design a solution for this problem which is ways different from traditional approaches. The proposed method is based on co-cluster sparse matrix learning (CCSML) method. This algorithm learns without reference class, and even with data continuous missing rate as high as the existing techniques. This method is based on a tensor optimization model and labeled maximum block. The computational models of sparse recovery learning are based on low-rank matrix and co-clusters of genome-wide association study (GWAS) data matrices, and the performance is better than existing techniques.

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Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle

Applied Sciences ◽

10.3390/app10062178 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2178 ◽

Cited By ~ 1

Author(s):

Bo-Lin Jian ◽

Wen-Lin Chu ◽

Yu-Chung Li ◽

Her-Terng Yau

Keyword(s):

Image Fusion ◽

Search Algorithm ◽

Matrix Decomposition ◽

Night Vision ◽

Experimental Results ◽

Low Rank ◽

Image Capture ◽

Rank Matrix ◽

Low Rank Matrix ◽

Consistency Verification

This study proposed the concept of sparse and low-rank matrix decomposition to address the need for aviator’s night vision goggles (NVG) automated inspection processes when inspecting equipment availability. First, the automation requirements include machinery and motor-driven focus knob of NVGs and image capture using cameras to achieve autofocus. Traditionally, passive autofocus involves first computing of sharpness of each frame and then use of a search algorithm to quickly find the sharpest focus. In this study, the concept of sparse and low-rank matrix decomposition was adopted to achieve autofocus calculation and image fusion. Image fusion can solve the multifocus problem caused by mechanism errors. Experimental results showed that the sharpest image frame and its nearby frame can be image-fused to resolve minor errors possibly arising from the image-capture mechanism. In this study, seven samples and 12 image-fusing indicators were employed to verify the image fusion based on variance calculated in a discrete cosine transform domain without consistency verification, with consistency verification, structure-aware image fusion, and the proposed image fusion method. Experimental results showed that the proposed method was superior to other methods and compared the autofocus put forth in this paper and the normalized gray-level variance sharpness results in the documents to verify accuracy.

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Automated Tire Visual Inspection Based on Low Rank Matrix Recovery

10.21203/rs.3.rs-109309/v1 ◽

2020 ◽

Author(s):

Guangxu Li ◽

Zhouzhou Zheng ◽

Yuyi Shao ◽

Jinyue Shen ◽

Yan Zhang

Keyword(s):

Visual Inspection ◽

Sparse Matrix ◽

Industrial Applications ◽

Convergence Speed ◽

Low Rank ◽

Inspection System ◽

Matrix Recovery ◽

Rank Matrix ◽

Low Rank Matrix Recovery ◽

Low Rank Matrix

Abstract Visual inspection is a challenging and widely employed process in industries. In this work, an automated tire visual inspection system is proposed based on low rank matrix recovery. Deep Network is employed to perform texture segmentation which benefits low rank decomposition in both quality and computational efficiency. We propose a dual optimization method to improve convergence speed and matrix sparsity by incorporating the improvement of the soft-threshold shrinkage operator by the weight matrix M. We investigated how incremental multiplier affects the decomposition accuracy and the convergence speed of the algorithm. On this basis, image blocks were decomposed into low-rank matrix and sparse matrix in which defects were separated. Comparative experiments have been performed on our dataset. Experimental results validate the theoretical analysis. The method is promising in false alarm, robustness and running time based on multi-core processor distributed computing. It can be extended to other real-time industrial applications.

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Principal Component Pursuit with Weighted Nuclear Norm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.1722 ◽

2014 ◽

Vol 513-517 ◽

pp. 1722-1726

Author(s):

Qing Shan You ◽

Qun Wan

Keyword(s):

Sparse Matrix ◽

Principal Component ◽

Low Rank ◽

Linear Measurements ◽

Alternating Direction ◽

Rank Matrix ◽

Small Set ◽

Principal Component Pursuit ◽

Low Dimensional ◽

Low Rank Matrix

Principal Component Pursuit (PCP) recovers low-dimensional structures from a small set of linear measurements, such as low rank matrix and sparse matrix. Pervious works mainly focus on exact recovery without additional noise. However, in many applications the observed measurements are corrupted by an additional white Gaussian noise (AWGN). In this paper, we model the recovered matrix the sum a low-rank matrix, a sparse matrix and an AWGN. We propose a weighted PCP for the recovery matrix, which is solved by alternating direction method. Numerical results show that the reconstructions performance of weighted PCP outperforms the classical PCP in term of accuracy.

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