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Research on Multi-level Attention-based Human Pose Estimation 2

Research on Multi-level Attention-based Human Pose Estimatio...

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2nd international conference on Algorithms, High Performance computing and Artificial Intelligence, AHPCAI 2022

作者： Gao, Jun Huang, Hua Li, Qishen Xia, Shaoqi School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Detecting human key points from a single image is very challenging due to occlusion, blurring, illumination and scale changes. In this paper, this problem is addressed by designing an effective network structure. Since global and local information plays an important role in reasoning about human body structure and invisible keypoints, Multi-level Attention Network (MAN) is proposed. First, compared with traditional multi-resolution networks, it enables multi-resolution feature maps with greater information variance by generating them directly from the highest resolution feature map, which in turn increases the abundance of feature information after final fusion. Secondly, it effectively integrates global and local information in different resolution feature maps through the Feature Alignment Attention Block(FAAB), and intensifies them in a targeted manner. On the COCO dataset, with HRNet (Sun K. et al [1]) as the baseline network, HRNet of inserted MAN improves 1.1-2.3 AP points over the baseline network. © 2022 IEEE.

关键词： attention mechanism Human Pose Estimation Keypoint detection

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Fault pattern recognition Method for DC-DC Power by Using Output Voltage Waveform Analysis 4

Fault Pattern Recognition Method for DC-DC Power by Using Ou...

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4th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2022

作者： Yuan, Wei Gao, Huizhuang Li, Wei Wang, Changxin The Fourth Military Representative Office Beijing Army Armament Department Beijing China China Aerospace Science & Industry Crop Defense Technology R&T Center Beijing China Beihang University School of Reliability and Systems Engineering Beijing China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

DC-DC power module is the power supply for electronic system. With its wide range of applications in engineering equipment, the degradation and fault conditions have received increasing attention. Many researches on DCDC power tend to take some of their internal devices out of the test and analysis, and study the corresponding test methods, which are rapidly developing in single fault diagnosis. In this paper, a method is proposed for identifying fault patterns of DC-DC power. The fault characteristics of the internal components in the power module are determined by PSpice circuit simulation. The test signals are the output current pulse signal and the normal input voltage signal. After inputting the test signal, the fault diagnosis of optocoupler and MOSFET tube is realized by analyzing the output waveform. The result shows that the optocoupler has a great influence on the adjustment ability of the circuit anti-jamming. MOSFET tube has a great influence on the output voltage level. © 2022 IEEE.

关键词： Signal analysis

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Computer Vision for Economic Activity Monitoring

Computer Vision for Economic Activity Monitoring

引用

Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), international conference on

作者： K. Santhanalaxmi Ryam Majed Khazal Nada Qasim Mohammed Diana Erudiyanathan Hameed Hassan Khalaf D Rajkumar Pillay School of Management Sastra Deemed to be University Thanjavur Department of Pharmacy Mazaya University College Iraq Department of Technology Al-Nisour University College Nisour Seq. Karkh Baghdad Iraq Department of Management Kristu Jayanti College (Autonomous) Bengaluru Karnataka Department of Management AI-Manara College for Medical Sciences (Maysan) Iraq Department of Management Operations and Analytic Institute of Public Enterprise Hyderabad

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

One of the most creative methods for real-time economic pattern analysis and interpretation is the use of computer vision in economic activity monitoring. The purpose of this study is to investigate computer vision technologies and assess their potential for automating the collection and analysis of visual data. If they did, it would allow for quicker and more accurate evaluations of economic activity. This study demonstrates how computer vision may be used to track several economic indicators, such as retail sales, the growth of infrastructure, and agricultural productivity. This is achieved through the use of complex algorithms for image recognition, pattern analysis, and object detection. The study highlights the benefits of using computer vision in place of more conventional data collection methods. Its effectiveness, scalability, and ability to provide thorough insights into the operation of the economic system are a few of these benefits. The purpose of this research is to demonstrate how computer vision can enhance decision-making processes for businesses and policymakers by giving them a better knowledge of patterns of economic activity. With the aid of case studies and experimental evaluations, this work was effectively finished. These results should be considered since they could have significant effects on how better policy planning and economic forecasting are done.

关键词： Computer vision Visualization Image recognition Economic indicators Scalability Decision making Economic forecasting Real-time systems pattern analysis Monitoring

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Web Application System of Handwritten Text recognition 5

Web Application System of Handwritten Text Recognition

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5th international conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Bodnia, Yevhen Kozulia, Mariia Natl Tech Univ Kharkiv Polytech Inst Kirpichova Str 2 Kharkiv Ukraine

The problem of text recognition is becoming increasingly important due to the active introduction of digital computing and the widespread use of word processors. pattern recognition is one of the most difficult from a mathematical point of view and one of the most popular areas of artificial intelligence programming. In the work is researched approaches and methods of solving text recognition problem, improved the performance of the available algorithms for text recognition and created algorithmic software. According to the analysis, neural networks were selected for handwriting recognition. The main advantage of using neural networks is a good generalization ability, the ability to use context analysis and recognize a symbol based on the surrounding symbols. The software implementation features of Hopfield and convolutional neural network, genetic algorithm, which were chosen as effective methods for recognizing handwritten text, were considered. Algorithmic software and web application that uses these methods for the task of handwritten text recognition is developed.

关键词： Handwritten text recognition pattern recognition recognition methods neural network genetic algorithm convolutional neural network Hopfield neural network machine learning data models

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Data analysis of Global Perceived Stress Scores during Covid-19 4

Data analysis of Global Perceived Stress Scores during Covid...

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4th international conference on Circuits, Control, Communication and computing, I4C 2022

作者： Muttaraju, Chakita Kabber, Anusha Parimala, S. Anagani, Prajwal Srinivasa, Gowri PES University Department of Computer Science and Engineering Bengaluru India PES University PES Center for Pattern Recognition Dept. of CSE Bengaluru India

ISBN: (纸本)9798350397475

The Covid-19 Pandemic that broke out in late December 2019 has had a widespread negative effect on the mental health of people around the world. This work aims to elicit features that had a major influence on mental health during the pandemic to better understand preventive measures and remedial actions that can be taken to help individuals in need. Along with factors such as demographic age, gender, marital status, and employment status, additional information such as the effect of media used as a source of information, coping methods, trust in the country's government, and healthcare organizations was analyzed to find their correlation (if any) to the perceived stress of the individual. Machine Learning techniques such as XGBoost, AdaBoost, Decision Trees, Ordinal regression, k-Nearest Neighbors, Lasso and Ridge regression were used to arrive at a relationship between the perceived stress scores and the features considered. On interpreting results from the different models, we conclude that the main factor influencing stress scores was loneliness followed by features indicating trust in government, compliance with Covid-19 preventive measures and concerns regarding the pandemic. © 2022 IEEE.

关键词： Decision trees

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Look Closer to Touch Behavior-enabled Android pattern Locks: A Study in the Wild

Look Closer to Touch Behavior-enabled Android Pattern Locks:...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Gergely Tuskó Weizhi Meng Brooke Lampe SPTAGE Lab DTU Compute Technical University of Denmark Denmark

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Android pattern lock is one of the most popular unlocking mechanisms on the Android platform. In order to enhance the security of Android’s unlocking functionality, a number of researchers have tried to combine touch biometrics with the pattern lock. Several studies have reported adequate— even excellent—authentication results, but, unlike fingerprint-and face recognition-based authentication, no biometrics-enabled pattern lock exists in the Android marketplace. Furthermore, most studies of biometrics-enabled pattern locks were conducted in controlled lab environments. As such, in this work, our goal is to investigate and validate the performance of touch behavior-enabled Android pattern locks in a more practical scenario, in which users have to download the application and learn to use it by themselves (as was often the case during the pandemic). During the course of our investigation, we collected substantial data, namely, the properties provided by the Android API for motion events, as well as measurements that could be extracted from the devices’ sensors. Our investigation found that touch-enabled Android pattern locks could achieve an average equal error ratio of 23.5% for user authentication—without changing the process from the user’s perspective. Next, we modified the user experience such that each user would train the authenticator with different fingers, similar to fingerprint-based authentication. With this modification, an average equal error ratio of 13.5% was achieved.

关键词： Privacy Pandemics Operating systems Face recognition Authentication Fingerprint recognition User experience

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A Lightweight Brain Tumor Segmentation Network Based on 3D Inverted Residual Modules 11

A Lightweight Brain Tumor Segmentation Network Based on 3D I...

引用

11th international conference on computing and pattern recognition, ICCPR 2022

作者： Liu, Yuchao Du, Xia Wang, Da-Han Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Semantic segmentation technology based on deep learning has played an important role for doctors in identifying brain tumor regions and formulating treatment plans. Popular automated segmentation methods for brain tumors include 2D and 3D convolution networks. The 3D networks give better results but lead to a significant increase in parameters and computational cost. In this paper, we propose a lightweight brain tumor segmentation network composed of 3D inverted residual modules, which can significantly reduce the computational complexity of 3D models. Based on a lightweight depthwise separable convolution, our 3D inverted residual module extracts high-dimensional brain tumor features through an intermediate expansion layer, thus improving performance. On the brain tumor dataset BraTS 2018, our network achieves dice scores of 80.8%, 90.7%, and 84.3% (for ET, WT, and TC, respectively) with only 0.68M parameters and 51.46G FLOPs. The results show that our method can significantly reduce the complexity of the 3D model and achieve very competitive performance. © 2022 ACM.

关键词： Semantics

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pattern recognition Algorithm to Detect Suspicious Activities

Pattern Recognition Algorithm to Detect Suspicious Activitie...

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Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： V C Mahavishnu S G Vikhas T Shyamganesh S Roopakumar S V Kavin Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

Detecting suspicious activities in public places with higher people gathering and interaction has turned out to be an act with growing interest due to the increasing number of crime scenes and causalities happening in these days. Surveying and tracking of human activities are increasingly difficult owing to the random nature of human movements and actions. The reliability is greatly affected due to this randomness. Also a human operator cannot continuously monitor multiple screens efficiently in a consequent manner so an automated surveillance system deployment becomes a necessity. Currently, tracking individuals may be done remotely, and the analysis of the recorded images can be automated using object detection models, with the help of high resolution cameras and the development of machine learning techniques. This proposed system aims in identifying threats that are probable to occur in a public gathering or space which may be an explosion, accident or possession of armoury, etc. This proposed model takes advantage of the information from the image data to learn complex patterns and develop pattern recognition technique to identify the anomalies using high resolution camera and alert the monitoring authority in order to take the necessary actions. This proposed work compares various object detection techniques of machine learning algorithms and suggests the best model based on its performance metrics.

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Palm Vein Image Processing and Enhancement in Vein pattern recognition System on FPGA 2

Palm Vein Image Processing and Enhancement in Vein Pattern R...

引用

2nd international conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2022

作者： Vats, Pranjal Tadepalli, Siva Kumar Electronics Engineering National Institute of Technology Pauri Uttarakhand Garhwal India

ISBN: (数字)9781665488754

ISBN: (纸本)9781665488754

Biometric recognition systems based on hand vein patterns are gaining popularity because they have qualities such as universality, uniqueness, and fraud resistance. Veins are not visible to the human eye since they are positioned beneath the skin, they constitute a strong barrier against deception. The hand's complicated vascular pattern allows for the estimation of a useful number of characteristics for identifying individuals. It has a sanitary advantage over other biometric authentication systems because it is contactless. In a real-world environment, image processing is critical. It's employed in a variety of applications, including security systems, artificial intelligence (AI), and so on. The Field Programmable Gate Array (FPGA) is the best-embedded platform for image processing applications. An FPGA's concurrent architecture allows it to do high-speed picture processing, allowing it to deliver timely warnings. Furthermore, modern FPGAs, particularly small-scale FPGAs, are very effi-cient in terms of cost and power usage. When compared to CPU execution, FPGA video/image computing delivers a processing speedup of ten times. This paper covered filtering approaches, contrast enhancement schemes and segmentation approaches. This paper demonstrates image enhancing techniques and then after results of these techniques are applied on vein images. We implemented an IP based embedded system because it is better in terms of power consumption, area and time. This paper has been work on reducing power as well as hardware utilization to design an optimized system since image processing requires extra hardware and power also. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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MB-ZZLBP: Multiscale Block ZigZag Local Binary pattern for Face recognition 2nd

MB-ZZLBP: Multiscale Block ZigZag Local Binary Pattern for F...

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2nd international conference on Machine Learning, Advances in computing,Renewable Energy and communication, MARC 2020

作者： Karanwal, Shekhar Diwakar, Manoj Dehradun Uttarakhand India

ISBN: (纸本)9789811623530

This work presents the LBP variant so-called MB-ZZLBP under illumination and expression variations. In MB-ZZLBP, initially mean is computed for all the square sub-blocks (size 2 × 2) of the 6 × 6 pixel window. After the mean computation, 3 × 3 window (pixel) is produced. Then ZigZag pixels are compared with each other, or in other words, the higher-order pixels are subtracted from the lower-order pixels. Differences of those which produce value ≥0 are allocated the label 1, else 0. After encoding each pixel position (binary pattern) eventually results in MB-ZZLBP transformed image. The MB-ZZLBP image is further divided into 3 × 3 sub-regions for extraction of histograms. The fused histogram is the feature size of MB-ZZLBP. The proposed FR approach attains remarkable results on EYB and Faces94 databases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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