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arXiv 2021年

作者： Cao, Congqi Lu, Yue Zhang, Yifan Jiang, Dongmei Zhang, Yanning Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Lab on Speech and Image Information Processing School of Computer Science Northwestern Poly-technical University Xi'an710129 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China

Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. However, directly modeling the contextual information between any two points brings huge cost in computation and memory, especially for action recognition, where there is an additional temporal dimension. Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition. The global context is factorized into sparse relation maps. We model the relationship between points in the same line along the direction of horizon, vertical and depth at each time, which forms a 3D criss-cross structure, and duplicate the same operation with recurrent mechanism to transmit the relation between points in a line to a plane finally to the whole spatiotemporal space. Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25% and 30% for video context modeling. We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the optimal way to factorize and fuse the relation maps. Comparisons with other state-of-the-art methods demonstrate the effectiveness and efficiency of our model. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Automatic Docking Design of Flange Pipe Based on Spatial information

Automatic Docking Design of Flange Pipe Based on Spatial Inf...

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International Conference on information Science and Control Engineering (ICISCE)

作者： Xingxun Li Xiuhua Zhang Jinmeng Wu Jian Huang Pianpian Chen Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan China Hubei Engineering Research Center of Video Image and HD Projection Wuhan Institute of Technology Wuhan China

With the development of ship industry, pipeline system has been more and more widely used. However, the pipeline site space is narrow. Mechanical and electrical equipments are of all kinds. The pipeline cable is densely distributed. All problems mentioned above seriously affects the pipeline construction. In order to solve the problem of pipeline docking, the method of making model and comprehensive lofting is usually adopted. However, this method is technically backward. The operation is inconvenient due to the narrow onsite space. The required period is long. With low information utilization and accuracy, the method of making model and comprehensive lofting has a great influence on the development cycle and design quality. In order to solve these problems, this paper presents a design method and system of flange pipe docking. A binocular camera is used to obtain the image of the pipe joint. The center position of the flange surface and array holes on the flange surface are extracted from the image, which is bias of subsequent work. First, the path of the intermediate connecting pipe is calculated. Then, according to the pipe standards of intermediate connection pipe and production specification of enterprise, the model of intermediate connecting pipe is automatically generated by using secondary development technology of CAD. In this paper, the binocular vision technology is combined with pipeline docking design, which significantly improves the utilization rate of field space information and design efficiency, and solves the problems of inconvenient operation and safety risk of traditional profiling method.

关键词： Industries Solid modeling Design methodology Pipelines Production Flanges Feature extraction

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The Intelligent Preparation of Scene Matching Guidance Reference Map Based on Prior Knowledge

The Intelligent Preparation of Scene Matching Guidance Refer...

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Pan Chen Yaozong Zhang Jinmeng Wu Xingxun Li School of Electrical Information Wuhan Institute of Technology Wuhan China Hubei Engineering Research Center of Video Image and HD Projection Wuhan China Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

Aiming at the problem that the automatic processing level of reference map preparation in image matching guidance is not high enough, our work proposes a novel reference map preparation based on the prior knowledge such as infrared imaging conditions and trajectory information. In the image preprocessing stage, the deep convolution network is used to extract the regions of interest automatically and meanwhile, combined with the analysis of connected region, the ROI extraction results can be optimized; Then, the natural scene is classified by texture of material, and the periodic temperature change of the feature is automatically mapped to the gray level, which conforms to the actual imaging characteristics and reduces the artificial gray level error. Next, combined with trajectory information and perspective transformation, multi-scale and multi-view scene images can be automatically obtained, which can avoid the situation that the manually selected target or landmark is not obvious or does not exist in the actual imaging. With comprehensive analysis from three aspects of reliability, stability and uniqueness, the best matching area is automatically determined, which improves the accuracy of matching algorithm and the time performance of reference map preparation system.

关键词： Temperature Convolution Target recognition Real-time systems Surface treatment Knowledge engineering Atmospheric modeling

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CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2020年第2期2卷 182-193页

作者： Zhang, Shifeng Liu, Ajian Wan, Jun Liang, Yanyan Guo, Guodong Escalera, Sergio Escalante, Hugo Jair Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Faculty of Information Technology Macau University of Science and Technology 999078 China Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Institute of Deep Learning Beijing100085 China Óptica y Electrónica Instituto Nacional de Astrofísica Puebla08007 Mexico Computer Science Department CINVESTAV-Zacatenco Mexico City07360 Mexico University of Chinese Academy of Sciences Beijing100049 China Westlake University Hangzhou310024 China

Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://***/***/face-anti-spoofing/welcome/challengecvpr2019?authuser=0. © 2019 IEEE.

关键词： Face recognition

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

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

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International Conference on information Science and Control Engineering (ICISCE)

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

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.

关键词： Computer vision Three-dimensional displays Shape Shape measurement Measurement uncertainty Measurement by laser beam Glass

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DCFH: A dynamic clustering approach based on fire hawk optimizer in flying ad hoc networks

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Vehicular Communications 2024年 47卷

作者： Hosseinzadeh, Mehdi Ali, Saqib Ahmad, Husham Jawad Alanazi, Faisal Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Rahmani, Amir Masoud Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Communication and Computer Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Kurdistan Region Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

In flying ad hoc networks (FANETs), unmanned aerial vehicles (UAVs) communicate with each other without any fixed infrastructure. Because of frequent topological changes, instability of wireless communication, three-dimensional movement of UAVs, and limited resources, especially energy, FANETs deal with many challenges, especially the instability of UAV swarms. One solution to address these problems is clustering because it maintains network performance and increases scalability. In this paper, a dynamic clustering scheme based on fire hawk optimizer (DCFH) is proposed for FANETs. In DCFH, each cluster head calculates the period of hello messages in its cluster based on its velocity. Then, a fire hawk optimizer (FHO)-based dynamic clustering operation is carried out to determine the role of each UAV (cluster head (CH) or cluster member (CM)) in the network. To calculate the fitness value of each fire hawk, a fitness function is suggested based on four elements, namely the balance of energy consumption, the number of isolated clusters, the distribution of CHs, and the neighbor degree. To improve cluster stability, each CH manages the movement of its CMs and adjusts it based on its movement in the network. In the last phase, DCFH defines a greedy routing process to determine the next-hop node based on a score, which consists of distance between CHs, energy, and buffer capacity. Finally, DCFH is simulated using the network simulator version 2 (NS2), and its performance is compared with three methods, including the mobility-based weighted cluster routing scheme (MWCRSF), the dynamic clustering mechanism (DCM), and the Grey wolf optimization (GWO)-based clustering protocol. The simulation results show that DCFH well manages the number of clusters in the network. It improves the cluster construction time (about 55.51%), cluster lifetime (approximately 11.13%), energy consumption (about 15.16%), network lifetime (about 2.6%), throughput (approximately 3.9%), packet deliver

关键词： Artificial intelligence (AI) Clustering Flying ad hoc networks (FANETs) Optimization algorithms Unmanned aerial vehicles (UAVs)

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Identification and Extraction of Circular Markers in 3D Reconstruction

Identification and Extraction of Circular Markers in 3D Reco...

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Yunfei Wang Hanyu Hong Jinmeng Wu Guangya Wang Shiyang Li Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan China Hubei Engineering Research Center of Video Image and HD Projection Wuhan China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

This paper presents a method for the identification and location of circular markers in 3D reconstruction. The input image is processed with gray scale and gaussian filter to remove noise interference and improve the accuracy of edge detection. The size and direction of the gradient of the image were calculated to obtain the edge features of the circular marker, and the circle degree of the closed edge was judged, and the center of the marker was obtained by fitting the edge features with the ellipse of the least square method. Hu moment similarity of each marker was obtained, and the non-marker was deleted and the real marker was retained by similarity judgment. Experiments show that the algorithm has good robustness, high precision, can effectively reduce the probability of false detection, and has high accuracy in complex environments.

关键词： Image edge detection Transforms Three-dimensional displays Fitting Feature extraction Cameras Least mean squares methods

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An intelligent clustering scheme based on whale optimization algorithm in flying ad hoc networks

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Vehicular Communications 2024年 49卷

作者： Hosseinzadeh, Mehdi Tanveer, Jawad Alanazi, Faisal Aurangzeb, Khursheed Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Lee, Sang-Woong Rahmani, Amir Masoud Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Computer Engineering College of Computer and Information Sciences King Saud University P. O. Box 51178 Riyadh 11543 Saudi Arabia Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Kurdistan Region Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan

Due to the progress of unmanned aerial vehicles (UAVs), this new technology is widely applied in military and civilian areas. Multi-UAV networks are often known as flying ad hoc networks (FANETs). Due to these applications, FANET must ensure communication stability and have high scalability. These goals are achieved by presenting clustering techniques in FANETs. However, the characteristics of these networks, like high-mobility nodes, limited energy, and dynamic topology, have created great challenges in two important processes of clustering protocols, namely cluster construction and the selection of cluster heads. In this paper, an intelligent clustering scheme based on the whale optimization algorithm called ICW is suggested in flying ad hoc networks. Firstly, each UAV specifies its hello interval based on the lifespan of adjacent links to guarantee the adaptability of ICW to FANET. Then, a centralized clustering process is done using a whale optimization algorithm (WOA) to find the best cluster centers on the network. To determine the membership of each UAV in a cluster, ICW employs a new criterion, i.e. closeness ratio, so that each UAV joins a cluster with the best closeness ratio. In addition, the evaluation of each whale is carried out based on a fitness function, consisting of three components, namely the number of isolated clusters, the ratio of inter-cluster distance to intra-cluster distance, and cluster size. Then, a cluster head is selected for each cluster based on a score value. This score is dependent on the weighted sum of four metrics, namely remaining energy, the average link lifespan between each UAV and its neighbors, neighbor degree, and the average distance between each UAV and its neighbors. In the last step, two routing processes, namely intra-cluster routing and inter-cluster routing, are introduced in FANET. Then, the evaluation and implementation of ICW is performed through the NS2 simulator. After completing the simulation process, ICW i

关键词： Artificial Intelligence (AI) Clustering Flying ad hoc networks (FANETs) Nature-inspired algorithms Unmanned aerial vehicles (UAVs)

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Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution

arXiv

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Zhou, Jiantao Yang, Jian Qi, Guo-Jun The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China The Research Center for Industries of the Future The School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States

Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the capabilities of Transformers that need to incorporate contextual information to extract features dynamically are neglected. To address this issue, we propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer. Specifically, in the CT block, we first propose a CNN-based Cross-Scale information Aggregation Module (CIAM) to enable the model to better focus on potentially helpful information to improve the efficiency of the Transformer phase. Then, we design a novel Cross-receptive Field Guided Transformer (CFGT) to enable the selection of contextual information required for reconstruction by using a modulated convolutional kernel that understands the current semantic information and exploits the information interaction within different self-attention. Extensive experiments have shown that our proposed CFIN can effectively reconstruct images using contextual information, and it can strike a good balance between computational cost and model performance as an efficient model. Source codes will be available at https://***/IVIPLab/CFIN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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One-shot distributed algorithm for generalized eigenvalue problem

arXiv

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arXiv 2020年

作者： Lyu, Kexin He, Fan Huang, Xiaolin Yang, Jie Chen, Liming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Ecole Centrale de Lyon France

Nowadays, more and more datasets are stored in a distributed way for the sake of memory storage or data privacy. The generalized eigenvalue problem (GEP) plays a vital role in a large family of high-dimensional statistical models. However, the existing distributed method for eigenvalue decomposition cannot be applied in GEP for the divergence of the empirical covariance matrix. Here we propose a general distributed GEP framework with one-shot communication for GEP. If the symmetric data covariance has repeated eigenvalues, e.g., in canonical component analysis, we further modify the method for better convergence. The theoretical analysis on approximation error is conducted and the relation to the divergence of the data covariance, the eigenvalues of the empirical data covariance, and the number of local servers is analyzed. Numerical experiments also show the effectiveness of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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