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SSRN 2023年

作者： Tian, Keyun Hong, Hanyu Zhang, Xiuhua Peng, Cong Wu, Yuanzhe Zhu, Cheng Hubei Provincial Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China Hubei Provincial Video Image and HD Projection Engineering Technology Research Center Wuhan Institute of Technology Wuhan430205 China School of Electrical Information Wuhan Institute of Technology Wuhan430205 China School of Mechanical and Electrical Engineering Wuhan Institute of Technology Wuhan430205 China

To address the low efficiency of ore detection methods caused by the complex lighting environment of mining sites, an all-weather real-time ore detection method based on near-infrared structured light and zero-crossing points characteristics was proposed. Firstly, an image acquisition method based on the physical model of infrared radiation is proposed to avoid structured light imaging being disturbed by a strong sunlight environment;Then, the centerline of the light bar is extracted quickly and accurately based on the zero-crossing point characteristics;Finally, the light bar neighborhood splicing detection method was proposed to realize the real-time detection and 3D measurement of the target ore. The experimental results show that the proposed method can effectively resist strong sunlight interference and faster detection speed, the average extraction time of light bar centerline per frame is below 1.8ms, and the error rate of ore size detection is less than 5%. © 2023, The Authors. All rights reserved.

关键词： Infrared radiation

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Learning with asymmetric kernels: Least squares and feature interpretation

arXiv

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arXiv 2022年

作者： He, Mingzhen He, Fan Shi, Lei Huang, Xiaolin Suykens, Johan A.K. Institute Of Image Processing And Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory For Contemporary Applied Mathematics Fudan University Shanghai200433 China School Of Mathematical Sciences Fudan University Shanghai200433 China KU Leuven LeuvenB-3001 Belgium

Asymmetric kernels naturally exist in real life, e.g., for conditional probability and directed graphs. However, most of the existing kernel-based learning methods require kernels to be symmetric, which prevents the use of asymmetric kernels. This paper addresses the asymmetric kernel-based learning in the framework of the least squares support vector machine named AsK-LS, resulting in the first classification method that can utilize asymmetric kernels directly. We will show that AsK-LS can learn with asymmetric features, namely source and target features, while the kernel trick remains applicable, i.e., the source and target features exist but are not necessarily known. Besides, the computational burden of AsK-LS is as cheap as dealing with symmetric kernels. Experimental results on the Corel database, directed graphs, and the UCI database will show that, in the case asymmetric information is crucial, the proposed AsK-LS can learn with asymmetric kernels and performs much better than the existing kernel methods that have to do symmetrization to accommodate asymmetric kernels. © 2022, CC BY.

关键词： Directed graphs

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Research on Laser Measurement of 3D curved Glass Contour based on computer Vision 7

Research on Laser Measurement of 3D curved Glass Contour bas...

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7th International Conference on Information Science and Control Engineering, ICISCE 2020

作者： Wang, Peng Wu, Jinmeng Yan, Guilin Bian, Shuo Wuhan Institute of Technology Hubei Key Laboratory of Optical Information and Pattern Recognition Hubei Research Centre of Video Image and High Definition Projection Wuhan China School of Electrical and Information Engineering School of Mechanical and Electrical Engineering

ISBN: (纸本)9781728164069

In the mobile phone glass industry, curved glass is widely used. Whether the curved glass is measured with high efficiency and precision and whether the curved edge profile error can be evaluated directly affects the quality and service cycle of glass products. In view of the difficulty of accurate measurement and evaluation of 3D mobile phones curved glass profile, a visual measurement method of 3D mobile phones curved glass profile error based on laser scanning and shape template matching is proposed in this paper. Accurate measurement of mobile phone curved glass greatly improves the fit between curved glass and mobile phone and the efficiency of production. Therefore, this paper proposes a method for evaluating the contour degree of curved glass on mobile phones based on Open Cascade and OpenCV. This method uses the lasers and camera equipment to extract the profile outline of the curved glass, then extract the profile outline of the mobile phone curved glass model that has been drawn in 3D software. The extracted contour lines are matched by methods such as translation and rotation, and finally the distance between the corresponding points is calculated as the contour error. Experimental results show that the method is able to accurately measure the profile error and greatly reduce the measurement instability and have good robustness. © 2020 IEEE.

关键词： Efficiency

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NIR-to-VIS Face recognition via Embedding Relations and Coordinates of the Pairwise Features

NIR-to-VIS Face Recognition via Embedding Relations and Coor...

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2019 International Conference on Biometrics, ICB 2019

作者： Cho, MyeongAh Chung, Tae-Young Kim, Taeoh Lee, Sangyoun Yonsei University Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Korea Republic of

ISBN: (纸本)9781728136400

NIR-to-VIS face recognition is identifying faces of two different domains by extracting domain-invariant features. However, this is a challenging problem due to the two different domain characteristics, and the lack of NIR face dataset. In order to reduce domain discrepancy while using the existing face recognition models, we propose a 'Relation Module' which can simply add-on to any face recognition models. The local features extracted from face image contain information of each component of the face. Based on two different domain characteristics, to use the relationships between local features is more domain-invariant than to use it as it is. In addition to these relationships, positional information such as distance from lips to chin or eye to eye, also provides domain-invariant information. In our Relation Module, Relation Layer implicitly captures relationships, and Coordinates Layer models the positional information. Also, our proposed Triplet loss with conditional margin reduces intra-class variation in training, and resulting in additional performance *** from the general face recognition models, our add-on module does not need to pre-train with the large scale dataset. The proposed module fine-tuned only with CASIA NIR-VIS 2.0 database. With the proposed module, we achieve 14.81% rank-1 accuracy and 15.47% verification rate of 0.1% FAR improvements compare to two baseline models. © 2019 IEEE.

关键词： Face recognition

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Random Fourier Features for Asymmetric kernels

arXiv

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arXiv 2022年

作者： He, Mingzhen He, Fan Liu, Fanghui Huang, Xiaolin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 800 Dongchuan RD. Minhang District Shanghai200240 China Laboratory for Information and Inference Systems École Polytechnique Fédérale de Lausanne EPFL SV-BMC AA-B118 Lausanne1015 Switzerland

The random Fourier features (RFFs) method is a powerful and popular technique in kernel approximation for scalability of kernel methods. The theoretical foundation of RFFs is based on the Bochner theorem (Bochner, 2005) that relates symmetric, positive definite (PD) functions to probability measures. This condition naturally excludes asymmetric functions with a wide range applications in practice, e.g., directed graphs, conditional probability, and asymmetric kernels. Nevertheless, understanding asymmetric functions (kernels) and its scalability via RFFs is unclear both theoretically and empirically. In this paper, we introduce a complex measure with the real and imaginary parts corresponding to four finite positive measures, which expands the application scope of the Bochner theorem. By doing so, this framework allows for handling classical symmetric, PD kernels via one positive measure;symmetric, non-positive definite kernels via signed measures;and asymmetric kernels via complex measures, thereby unifying them into a general framework by RFFs, named AsK-RFFs. Such approximation scheme via complex measures enjoys theoretical guarantees in the perspective of the uniform convergence. In algorithmic implementation, to speed up the kernel approximation process, which is expensive due to the calculation of total mass, we employ a subset-based fast estimation method that optimizes total masses on a sub-training set, which enjoys computational efficiency in high dimensions. Our AsK-RFFs method is empirically validated on several typical large-scale datasets and achieves promising kernel approximation performance, which demonstrate the effectiveness of AsK-RFFs. © 2022, CC BY.

关键词： Computational efficiency

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VSGNet: visual saliency guided network for skin lesion segmentation

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Expert Systems with Applications 2025年

作者： Zhefei Cai Yingle Fan Tao Fang Wei Wu Laboratory of Pattern Recognition and Image Processing Hangzhou Dianzi University Hangzhou 310018 China Department of Biomedical Engineering University of Miami Florida 33124 USA Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang 310058 China

The accuracy of skin lesion segmentation is of great significance for the subsequent clinical diagnosis. In order to improve the segmentation accuracy, some pioneering works tried to embed multiple complex modules, or used the huge Transformer framework, but due to the limitation of computing resources, these type of large models were not suitable for the actual clinical environment. To address the coexistence challenges of precision and lightweight, we propose a visual saliency guided network (VSGNet) for skin lesion segmentation, which generates saliency images of skin lesions through the efficient attention mechanism of biological vision, and guides the network to quickly locate the target area, so as to solve the localization difficulties in the skin lesion segmentation tasks. VSGNet includes three parts: Color Constancy module, Saliency Detection module and Ultra Lightweight Multi-level Interconnection Network (ULMI-Net). Specially, ULMI-Net uses a U-shaped structure network as the skeleton, including the Adaptive Split Channel Attention (ASCA) module that simulates the parallel mechanism of biological vision dual pathway, and the Channel-Spatial Parallel Attention (CSPA) module inspired by the multi-level interconnection structure of visual cortices. Through these modules, ULMI-Net can balance the efficient extraction and multi-scale fusion of global and local features, and try to achieve the excellent segmentation results at the lowest cost of parameters and computational complexity. To validate the effectiveness and robustness of the proposed VSGNet on three publicly available skin lesion segmentation datasets (ISIC2017, ISIC2018 and PH2 datasets). The experimental results show that compared to other state-of-the-art methods, VSGNet improves the Dice and mIoU metrics by 1.84 % and 3.34 %, respectively, and with a 196 × and 106 × reduction in the number of parameters and computational complexity. This paper constructs the VSGNet integrating the biological vis

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AMST: Object tracking based on collaborative framework with adaptive multi-strategy

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Information Sciences 2025年 718卷

作者： Rui Xu Si Chen Yan Yan Da-Han Wang Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen 361024 China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen 361005 China

Deep object tracking can be modeled by online learning to adapt to the appearance changes, or offline learning to achieve fast tracking speed. However, either online or offline learning trackers are still difficult to continuously cope with challenging tracking scenes, due to the cumulative errors easily caused by online learning trackers and the object appearance changes often neglected by offline learning trackers. To overcome this problem, we propose a novel object tracking method based on collaborative framework with adaptive multi-strategy, enabling automatic switch between the online and offline learning trackers. In this framework, we first design the variable action set (VAS) module to adaptively choose the different trackers, tracking strategies, and template strategies with multiple action labels. Moreover, we incorporate the template update and selection (TUS) module, which dynamically updates and selects templates from a memory unit to adapt to object appearance changes. To update the action set and the memory unit, we further devise the online reliability evaluation (ORE) module that not only evaluates the tracking result and the tracker itself, but also estimates the quality and quantity of templates. Comprehensive experiments on challenging short-term and long-term tracking benchmarks demonstrate the remarkable performance of the proposed method.

关键词： Adaptive multi-strategy Collaborative framework Offline learning tracker Online learning tracker Template update and selection

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Generalization properties of hyper-RKHS and its applications

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 6220-6257页

作者： Fanghui Liu Lei Shi Xiaolin Huang Jie Yang Johan A.K. Suykens Department of Electrical Engineering ESAT-STADIUS KU Leuven Leuven Belgium Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai China Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

This paper generalizes regularized regression problems in a hyper-reproducing kernel Hilbert space (hyper-RKHS), illustrates its utility for kernel learning and out-of-sample extensions, and proves asymptotic convergence results for the introduced regression models in an approximation theory view. Algorithmically, we consider two regularized regression models with bivariate forms in this space, including kernel ridge regression (KRR) and support vector regression (SVR) endowed with hyper-RKHS, and further combine divide-and-conquer with Nyström approximation for scalability in large sample cases. This framework is general: the underlying kernel is learned from a broad class, and can be positive definite or not, which adapts to various requirements in kernel learning. Theoretically, we study the convergence behavior of regularized regression algorithms in hyper-RKHS and derive the learning rates, which goes beyond the classical analysis on RKHS due to the non-trivial independence of pairwise samples and the characterisation of hyper-RKHS. Experimentally, results on several benchmarks suggest that the employed framework is able to learn a general kernel function form an arbitrary similarity matrix, and thus achieves a satisfactory performance on classification tasks.

关键词： hyper-RKHS approximation theory kernel learning out-of-sample extensions

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2D License Plate recognition based on Automatic Perspective Rectification

2D License Plate Recognition based on Automatic Perspective ...

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International Conference on pattern recognition

作者： Hui Xu Zhao-Hong Guo Da-Han Wang Xiang-Dong Zhou Yu Shi Chongqing school University of Chinese Academy of Sciences china Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences

License plate recognition (LPR) remains a challenging task in face of some difficulties such as image deformation and multi-line character distribution. Text rectification that is crucial to eliminate the effects of image deformation has attracted increasing attentions in scene text recognition. However, current text rectification methods are not designed specifically for LPR, which did not take the features of plate deformation into account. Considering the fact that a license plate (LP) can only generate perspective distortion in the image due to its rigid feature, in this paper we propose a novel perspective rectification network (PRN) to automatically estimate the perspective transformation and rectify the distorted LP accordingly. For recognition, we propose a location-aware 2D attention based recognition network that is capable of recognizing both single-line and double-line plates with perspective deformation. The rectification network and recognition network are connected for end-to-end training. Experiments on common datasets show that the proposed method achieves the state-of-the-art performance, demonstrating the effectiveness of the proposed approach.

关键词： Training Text recognition Face recognition Licenses Benchmark testing Distortion Optical character recognition software

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MoNET: No-reference image quality assessment based on a multi-depth output network

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Journal of Electronic Imaging 2021年第4期30卷 043007-043007页

作者： Sang, Qingbing Su, Chenfei Zhu, Lingying Liu, Lixiong Wu, Xiaojun Bovik, Alan C. Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi China Beijing Institute of Technology School of Computer Science and Technology Beijing China University of Texas at Austin Laboratory for Image and Video Engineering AustinTX United States

When deep convolutional neural networks perform feature extraction, the features computed at each layer express different abstractions of visual information. The earlier layers extract highly compact low-level features such as bandpass and directional primitives, whereas deeper layers extract structural features of increasing abstraction, similar to contours, shapes, and edges, becoming less effable as the depth increases. We propose a different kind of end-to-end no-reference (NR) image quality assessment (IQA) model, which is defined as a multi-depth output convolutional neural network (MoNET). It accomplishes this by mapping both shallow and deep features to perceived quality. MoNET delivers three outputs that express shallow (lower-level) and deep (high-level) features, and maps them to subjective quality scores. The multiple outputs are combined into a single, final quality score. MoNET does this by combining the responses of three learning machines, so it may be viewed as a form of ensemble learning. The experimental results on three public image quality databases show that our proposed model achieves better performance than other state-of-the-art NR IQA algorithms. © 2021 SPIE and IS and T.

关键词： Convolutional neural networks

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