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Materials Today: Proceedings 2022年 52卷 1-6页

作者： Souad Lidaighbi Mustapha Elyaqouti Khalid Assalaou Dris Ben Hmamou Driss Saadaoui Jihad H'roura Materials and Renewable Energy Laboratory Agadir Faculty of Sciences Ibn Zohr University BP 8106 80000 Agadir Morocco Laboratory of Electronics Signal Processing and Physical Modeling Faculty of Sciences of Agadir Ibn Zohr University BP 8106 80000 Agadir Morocco Laboratory of Images and Pattern Recognition - Intelligent and Communicating Systems Faculty of Sciences of Agadir Ibn Zohr University BP 8106 80000 Agadir Morocco

Modeling photovoltaic (PV) modules is fundamental for analyzing their efficiency and performance under different operating conditions. Generally, photovoltaic module modeling is based on a suitable equivalent circuit and a set of parameters representing the PV module's properties. Defining these parameters is not a trivial task, as they are usually not included in the technical documentation of the module. Therefore, the need to find suitable values for the model parameters becomes imperative. In this paper, we will test and compare two methods of extracting single-diode model (SDM) parameters: analytical and numerical, under standard test conditions (STC conditions). We examined the performance of six techniques: three analytical and three numerical. Except for Saleem technique, which tends to underestimate the I-V and P-V curves in the MPP zone, the results demonstrated that both approaches (analytical and iterative/numerical) are valid and accurate for predicting the I-V and P-V curves under STC conditions.

关键词： Photovoltaic module Single-diode model Parameters extraction Analytical methods Numerical methods

FWLBP: A scale invariant descriptor for texture classification

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arXiv 2018年

作者： Roy, Swalpa Kumar Bhattacharya, Nilavra Chanda, Bhabatosh Chaudhuri, Bidyut B. Ghosh, Dipak Kumar Optical Character Recognition Laboratory Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India School of Information University of Texas AustinTX78712 United States Image Processing Laboratory Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata700108 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Rourkela769008 India

In this paper we propose a novel texture descriptor called Fractal Weighted Local Binary pattern (FWLBP). The fractal dimension (FD) measure is relatively invariant to scale-changes, and presents a good correlation with human viewpoint of surface roughness. We have utilized this property to construct a scale-invariant descriptor. Here, the input image is sampled using an augmented form of the local binary pattern (LBP) over three different radii, and then used an indexing operation to assign FD weights to the collected samples. The final histogram of the descriptor has its features calculated using LBP, and its weights computed from the FD image. The proposed descriptor is scale invariant, and is also robust in rotation or reflection, and partially tolerant to noise and illumination changes. In addition, the local fractal dimension is relatively insensitive to the bi-Lipschitz transformations, whereas its extension is adequate to precisely discriminate the fundamental of texture primitives. Experiment results carried out on standard texture databases show that the proposed descriptor achieved better classification rates compared to the state-of-the-art descriptors. Copyright © 2018, The Authors. All rights reserved.

关键词： Fractal dimension

Discriminant analysis with Gabor phase for robust face recognition

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Discriminant analysis with Gabor phase for robust face recog...

IAPR International Conference on Biometrics (ICB)

作者： Jianfei Zhu Dong Cao Sifei Liu Zhen Lei Stan Z. Li Institute of Intelligent Information Processing Xidian University Xi' an China Hohai University Nanjing Jiangsu Province China Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences Beijing Beijing CN

This paper presents an occlusion robust image representation method and apply it to face recognition. Inspired from the recent work [15], we propose a Gabor phase difference representation for occlusion robust face recognition. Based on the good ability of Gabor filters to capture image structure and the robustness to image occlusion shown in this paper, Gabor phase features are expected to be discriminative and robust for face representation in occlusion case. Besides, we adopt spectral regression based discriminant analysis with the extracted Gabor phase features to find the most discriminant subspace to classify different faces. In this way, an occlusion robust face image discriminant subspace is derived. Extensive experiments with various occlusion cases show the efficacy of the proposed method.

关键词： Face Robustness Face recognition Databases Kernel Training Principal component analysis

Modified majority voter for triple modular redundancy system

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Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology 2009年第SUPPL. 1期41卷 27-30页

作者： Zhang, Yi-Nan Guo, Ping Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing 100875 China Laboratory of Computer Science Institute of Software The Chinese Academy of Sciences Beijing 100080 China

When applying formal majority voter in TMR (triple modular redundancy) fault tolerance system with two error injections, there is a problem that formal majority voter has a low rate of output. To solve this problem, we propose a modified majority voting model with special rules. In the situation of error injection, test result shows that compared with formal majority voter, modified majority voter has a higher rate of correct decision and a lower ratio of benign signals.

关键词： Fault tolerance

Dim small targets fusion detection in infrared image

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Dim small targets fusion detection in infrared image

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Yu-Qiu Sun Yu Zheng Jin-Wen Tian Jian Liu School of Information and Mathematics Yangtze University Jingzhou China State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China State Education Commission Key Laboratory for Image Processing and Intelligent Control Department of Electronic Information and Enginery of Huazhong University of Science and Technology Wuhan China

To infrared images, the contrast of target and background is low, dim small targets have no concrete shapes and their textures cannot be reliable predicted. The paper puts forward a novel algorithm to fuse mid-wave and long-wave infrared images and detect targets. Firstly, the source images are decomposed by wavelet transformation. In usual, targets in infrared images are man-made, and their fractal dimension is different comparing with natural background. In wavelet transformation domain high-frequency part, we calculate local fractal dimension and set up fusion rule to merge corresponding sub-images of two matching source images. In low-frequency, we extract local maximum gray level to fuse them. Then reconstruct image by wavelet inverse transformation and obtain fused result image. In fusion results, the contrast between targets and background has obvious changes. And targets can be detected using contrast threshold. The experimental results show that the method proposed in this paper using wavelet transformation fractal dimension to fuse dual band infrared images, and then detect targets is better than using mid-wave or long -wave infrared images detect targets alone.

关键词： Fractals Wavelet transforms Pixel Object detection Fuses Approximation methods Manganese

PPT Fusion: Pyramid Patch Transformer for a Case Study in Image Fusion

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arXiv 2021年

作者： Fu, Yu Xu, Tianyang Wu, Xiao-Jun Kittler, Josef Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

The Transformer architecture has witnessed a rapid development in recent years, outperforming the CNN architectures in many computer vision tasks, as exemplified by the Vision Transformers (ViT) for image classification. However, existing visual transformer models aim to extract semantic information for high-level tasks, such as classification and detection. These methods ignore the importance of the spatial resolution of the input image, thus sacrificing the local correlation information of neighboring pixels. In this paper, we propose a Patch Pyramid Transformer(PPT) to effectively address the above issues. Specifically, we first design a Patch Transformer to transform the image into a sequence of patches, where transformer encoding is performed for each patch to extract local representations. In addition, we construct a Pyramid Transformer to effectively extract the non-local information from the entire image. After obtaining a set of multi-scale, multi-dimensional, and multi-angle features of the original image, we design the image reconstruction network to ensure that the features can be reconstructed into the original input. To validate the effectiveness, we apply the proposed Patch Pyramid Transformer to image fusion tasks. The experimental results demonstrate its superior performance, compared to the state-of-the-art fusion approaches, achieving the best results on several evaluation indicators. Thanks to the underlying representational capacity of the PPT network, it can directly be applied to different image fusion tasks without redesigning or retraining the network. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

An Optimizing Parameters and Feature Selection in SVM Based on Improved Cockroach Swarm Optimization 16th

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An Optimizing Parameters and Feature Selection in SVM Based ...

16th International Conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-The Yu, Jie Nguyen, Thi-Thanh-Tan Lai, Quoc-Anh Ngo, Truong-Giang Dao, Thi-Kien Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Department of Pattern Recognition & Image Processing Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (纸本)9789813367562

This study improves a classifier of the support vector machine (SVM) by optimizing its parameters by adjusting cockroach swarm optimization (CSO). Classification system design includes data inputs, pre-process, and classification. The relief technique selects the feature subset, and the kernel parameters used as input for the system. The feature selection and classification of data sets were figured out by the combination of ICSO and SVM with its optimized parameters synchronous. Simulation results of the proposed scheme are compared with other systems, e.g., GA-SVM, PSO-SVM, and CSO-SVM, which show that the proposed project can achieve better classification accuracy than the different algorithms. It is promising that the proposed scheme can be applied to high-dimensional data classification, such as image classification, signal classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Support vector machines

FVC2002: Second Fingerprint Verification Competition

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FVC2002: Second Fingerprint Verification Competition

International Conference on pattern recognition

作者： D. Maio D. Maltoni R. Cappelli J.L. Wayman A.K. Jain Biometric System Lab-DEIS University of Bologna Cesena Italy Biometric Test Center College of Engineering San Jose State University Foundation San Jose CA USA Pattern Recognition and Image Processing Laboratory Michigan State University East Lansing MI USA

ISBN: (纸本)076951695X

Two years after the first edition, a new Fingerprint Verification Competition (FVC2002) was organized by the authors, with the aim of determining the state-of-the-art in this challenging pattern recognition application. The experience and the feedback received from FVC2000 allowed the authors to improve the organization of FVC2002 and to capture the attention of a significantly higher number of academic and commercial organizations (33 algorithms were submitted). This paper discusses the FVC2002 database, the test protocol and the main differences between FVC2000 and FVC2002. The algorithm performance evaluation will be presented at the 16/sup th/ ICPR.

关键词： Fingerprint recognition Testing Protocols Biometrics Image databases pattern recognition Educational institutions Image processing Art Feedback

Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking

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arXiv 2022年

作者： Tang, Zhangyong Xu, Tianyang Li, Hui Wu, Xiao-Jun Zhu, XueFeng Kittler, Josef Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China The Center for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

We address the problem of multi-modal object tracking in video and explore various options of fusing the complementary information conveyed by the visible (RGB) and thermal infrared (TIR) modalities including pixel-level, feature-level and decision-level fusion. Specifically, different from the existing methods, paradigm of image fusion task is heeded for fusion at pixel level. Feature-level fusion is fulfilled by attention mechanism with channels excited optionally. Besides, at decision level, a novel fusion strategy is put forward since an effortless averaging configuration has shown the superiority. The effectiveness of the proposed decision-level fusion strategy owes to a number of innovative contributions, including a dynamic weighting of the RGB and TIR contributions and a linear template update operation. A variant of which produced the winning tracker at the Visual Object Tracking Challenge 2020 (VOT-RGBT2020). The concurrent exploration of innovative pixel- and feature-level fusion strategies highlights the advantages of the proposed decision-level fusion method. Extensive experimental results on three challenging datasets, i.e., GTOT, VOT-RGBT2019, and VOT-RGBT2020, demonstrate the effectiveness and robustness of the proposed method, compared to the state-of-the-art approaches. Code will be shared at https://***/Zhangyong-Tang/DFAT. © 2022, CC BY.

关键词： Pixels

Fast signal recovery from saturated measurements by linear loss and nonconvex penalties

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arXiv 2018年

作者： He, Fan Huang, Xiaolin Liu, Yipeng Yan, Ming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University The MOE Key Laboratory of System Control and Information Processing Shanghai200240 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China The Department of Computational Mathematics Science and Engineering Michigan State University MI United States

Sign information is the key to overcoming the inevitable saturation error in compressive sensing systems, which causes information loss and results in bias. For sparse signal recovery from saturation, we propose to use a linear loss to improve the effectiveness from existing methods that utilize hard constraints/hinge loss for sign consistency. Due to the use of linear loss, an analytical solution in the update progress is obtained, and some nonconvex penalties are applicable, e.g., the minimax concave penalty, the 0 norm, and the sorted 1 norm. Theoretical analysis reveals that the estimation error can still be bounded. Generally, with linear loss and nonconvex penalties, the recovery performance is significantly improved, and the computational time is largely saved, which is verified by the numerical experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Compressed sensing