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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16443 条记录，以下是961-970 订阅

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Detection of Indian Fake Currency using image processing

Detection of Indian Fake Currency using Image Processing

引用

2022 International conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

作者： Asha Banu, S.M. Sandhya, S. Sundari, T Vijaya Shri Ranjani, P.R. Dept of ECE Sri Krishna College of Engineering and Technology India

ISBN: (纸本)9781665489621

The fake currency notes are detected using image processing employing MATLAB in this paper. This project aims in providing the best techniques in image acquisition, and image segmentation. The work uses CANNY's algorithm to extract the notes' features much more effectively. algorithms for image processing are used to extract the options. The technique used here functions well with the recently introduced denominations of 500 and 2000. The recommended strategy provides a practical means of detecting fake currency that is supported by physical inspection. © 2022 IEEE.

关键词： Fake detection

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Optical Set up for Scanning System in Manufacturing Inspection

Optical Set up for Scanning System in Manufacturing Inspecti...

引用

2022 International conference on Applied Mathematics and Digital Simulation, AMDS 2022

作者： Salah, Yosri Ben Liu, Weiguo Tian, Ailing Sellami, Lamia Hamida, Ahmed Ben School of Optoelectronics Xi'An Technological University Xi'an China National School of Engineering Sfax University Sfax Tunisia

Essentially, 3D scanning systems are fundamental in today's industry, since there is a necessity for the automation of the whole manufacturing process with the aim to achieve better results in terms of accuracy, quality, and manufacturing time. Indeed, experts move towards the integration of high-resolution sensors, and optical devices alongside software, which may offer several advantages for large manufacturing units. In this paper, a stereo vision system is demonstrated in detail while performing a scan. The developed model could be used to carry out several measurement experiments, and the measurement results are analyzed, which proved the correctness and feasibility of the method. Delivered results and outcomes are discussed. © Published under licence by IOP Publishing Ltd.

关键词： Stereo image processing

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Research on Nonlinear Filtering Applied in Integrated Navigation System 23

Research on Nonlinear Filtering Applied in Integrated Naviga...

引用

6th International conference on Signal processing and Machine Learning, SPML 2023

作者： Wang, Zheng Guo, Jianxin Hu, Chengyu The School of Electronic Information Xijing University Xi an China

ISBN: (纸本)9798400707575

As the demands for real-world scenarios and positioning become increasingly complex, single positioning systems are limited in meeting these needs, leading the focus of positioning research to shift towards combined positioning methods, known as hybrid positioning. The goal of hybrid positioning is to integrate various positioning methods, to compensate for the limitations of single positioning systems, and to enhance positioning accuracy and performance. However, the state estimation problem in hybrid positioning is mostly a nonlinear filtering problem. The commonly used nonlinear filtering algorithms have drawbacks such as errors caused by model linearization. To implement a high-precision, real-time hybrid positioning system, a good filtering algorithm is needed. Therefore, this paper mainly focuses on the research of nonlinear filtering methods. Through computer simulation, this study compares the advantages and disadvantages of various nonlinear filtering algorithms and demonstrates that the improved particle filtering algorithm has significant advantages in terms of accuracy and real-time performance compared to traditional nonlinear algorithms, making it very suitable for subsequent applications in various hybrid positioning systems. © 2023 ACM.

关键词： Nonlinear filtering

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OneActor: Consistent Subject Generation via Cluster-Conditioned Guidance 38

OneActor: Consistent Subject Generation via Cluster-Conditio...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Wang, Jiahao Yan, Caixia Lin, Haonan Zhang, Weizhan Wang, Mengmeng Gong, Tieliang Dai, Guang Sun, Hao School of Computer Science and Technology MOEKLINNS Xi'an Jiaotong University China State Key Laboratory of Communication Content Cognition China College of Computer Science and Technology Zhejiang University of Technology China SGIT AI Lab State Grid Corporation of China Switzerland China Telecom Artificial Intelligence Technology Co.Ltd. China

Text-to-image diffusion models benefit artists with high-quality image generation. Yet their stochastic nature hinders artists from creating consistent images of the same subject. Existing methods try to tackle this challenge and generate consistent content in various ways. However, they either depend on external restricted data or require expensive tuning of the diffusion model. For this issue, we propose a novel one-shot tuning paradigm, termed OneActor. It efficiently performs consistent subject generation solely driven by prompts via a learned semantic guidance to bypass the laborious backbone tuning. We lead the way to formalize the objective of consistent subject generation from a clustering perspective, and thus design a cluster-conditioned model. To mitigate the overfitting challenge shared by one-shot tuning pipelines, we augment the tuning with auxiliary samples and devise two inference strategies: semantic interpolation and cluster guidance. These techniques are later verified to significantly improve the generation quality. Comprehensive experiments show that our method outperforms a variety of baselines with satisfactory subject consistency, superior prompt conformity as well as high image quality. Our method is capable of multi-subject generation and compatible with popular diffusion extensions. Besides, we achieve a 4× faster tuning speed than tuning-based baselines and, if desired, avoid increasing the inference time. Furthermore, our method can be naturally utilized to pre-train a consistent subject generation network from scratch, which will implement this research task into more practical applications. Project page: https://***/OneActor-webpage/. © 2024 Neural information processing systems foundation. All rights reserved.

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image Analysis System for Augmented Reality Games 13th

Image Analysis System for Augmented Reality Games

引用

13th International conference on image processing and Communications, IP and C 2023, 13th International conference on Computer Recognition systems, CORES 2023

作者： Garbat, Piotr Galiński, Grzegorz Żakowski, Michal Bieniek, Mateusz Lasocki, Maciej Institiute of Microelectronics and Optoelectronics Warsaw University of Technology Nowowiejska 15/19 St. Warsaw Poland Institiute of Radioelectronics and Multimedia Technology Warsaw University of Technology Nowowiejska 15/19 St. Warsaw Poland Faculty of Architecture Warsaw University of Technology Koszykowa 55 St. Warsaw Poland

ISBN: (纸本)9783031416293

This article presents the idea of a vision system allowing the analysis of images from multiple cameras for the needs of augmented reality systems. The presented solution allows for the effective determination of the positions and movement paths of people moving in urban space and their activities. The article presents the key modules of multi-view image analysis, including the gamer detection and tracking (MOT) subsystem and the user activity detection subsystem. The discussed subsystems have been tested in order to determine the effectiveness of the proposed algorithms for determining the movement path and recognizing activity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Augmented reality

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RESEARCH ON HIGH-SPEED MOTOR CONTROL BASED ON FPGA AND SEMI-PHYSICAL SIMULATION

RESEARCH ON HIGH-SPEED MOTOR CONTROL BASED ON FPGA AND SEMI-...

引用

2024 CSAA/IET International conference on Aircraft Utility systems, AUS 2024

作者： Wang, xinyuan Sun, Lili Zhang, Run AVIC Shaanxi Aero Electric Co. Ltd No. 17 Jinye 2nd Road Xi’an China

With the advancement of more electric aircraft, the Environmental Control System (ECS) has progressively shifted from the traditional engine bleed mode to an electric drive mode. This transition not only significantly simplifies the aircraft's structure but also effectively reduces pollutant emissions. At the heart of the Electric ECS (EECS), the High-Speed Permanent Magnet Synchronous Motor (HSPMSM) stands out for its exceptional performance, with each rotation cycle requiring mere milliseconds or less. To ensure the stable operation of the HSPMSM at such high speeds, the design of its control system faces formidable challenges, particularly stringent requirements on control cycle times and latency. In response, this paper proposes an architecture integrating FPGA and DSP for precise HSPMSM control, aiming to significantly enhance the response speed of the control system. Within the control strategy for the HSPMSM, we innovatively incorporate advanced functional module designs such as the Coordinate Rotation Digital Computer (CORDIC) algorithm, Proportional-Integral (PI) controllers, and Space Vector Pulse Width Modulation (S VPWM) by using the powerful parallel processing capabilities of FPGA. Compared with traditional controllers like DSP for HSPMSM applications, the successful integration of FPGA with advanced control algorithms can effectively shorten the control cycle. Besides, by constructing a hardware-in-the-loop (HIL) real-time simulation model, we successfully demonstrated the remarkable advantages of the FPGA-based control system in terms of real-time performance, stability, and control accuracy. © The Institution of Engineering & Technology 2024.

关键词： Hardware-in-the-loop simulation

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Research on image processing and Recognition algorithms in Software Information systems Based on Deep Learning

Research on Image Processing and Recognition Algorithms in S...

引用

Electrical Engineering, Big Data and algorithms (EEBDA), IEEE International conference on

作者： xiaojun Tang Radio and Television Monitoring Center of Shandong Province Jinan Shandong China

With the rapid development of deep learning technology, its application in image processing and recognition has become a hot research topic. The application of these technologies in software information systems such as automatic image classification, facial recognition, and medical image analysis not only improves processing efficiency but also enhances recognition accuracy. This article focuses on the training and deployment of adversarial generative network models in deep learning algorithms for graphic processing and recognition applications. Finally, two sets of simulation experiments were conducted, and the results are as follows: in the face recognition system, the recognition accuracy optimized by the deep learning algorithm based on adversarial generative networks improved by an average of 8.9% compared to traditional methods. In the traffic sign recognition system, the overall average recall index improved by 12.45%. This article proposes an improved network architecture and training strategy, which significantly improves the quality of image generation and the accuracy of recognition models in software information systems.

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Fake Face image Classification by Blending the Scalable Convolution Network and Hierarchical Vision Transformer 1

引用

4th International conference on Computer and Communication Technologies, IC3T 2022

作者： Kerenalli, Sudarshana Yendapalli, Vamsidhar Mylarareddy, C. Department of CSE SoT GITAM University Karnataka Bengaluru562163 India

ISBN: (数字)9789811985638

ISBN: (纸本)9789811985621

A face has been used as a primary and unique attribute to authenticate individual users in emerging security approaches. Cybercriminals use the double-edged sword "image processing" capabilities to deceive innocent users. The underlying technology is based on advanced machine learning and deep learning algorithms. The intentions of cyber criminals range from simple mimicking or trolling to creating violent situations in society. Hence, it is necessary to resolve such problems by identifying the fake face images generated by expert humans or artificial intelligent algorithms. Machine learning and artificial neural networks are used to resolve the issue. In this work, we have designed an approach for detecting deep learning-generated fake face images by combining the capabilities of the scalable convolutional neural networks (CNN) "EfficientNet" and hierarchical vision transformer (ViT) "shifted window transformer". The proposed method accurately classifies the fake face images with a 98.04% accuracy and a validation loss of 0.1656 on the 140 k_real_fake_faces image dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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A Survey on Crime Detection using CCTV systems 3

A Survey on Crime Detection using CCTV Systems

引用

3rd International conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Sheela, A Jeba Balaji, S. Balaji, B. Hemanth Kumar, U. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343632

In order to improve security measures across numerous domains, such as public safety, transportation, and critical infrastructure protection, the use of closed-circuit television (CCTV) systems for threat analysis has become increasingly important. The technical abstract gives a summary of the most recent algorithms and techniques used in threat analysis when using CCTV. This study emphasizes the importance of spatial and temporal reasoning, machine learning models like Support Vector Machines (SVM), Haar-Cascade, ViF (Violent Flows), OViF (Oriented Violent Flows), RINOC (Real-time image and Video Object Classification), and LRT (Live-Streaming Real-Time). Insights into the diverse methodologies that can be applied to enhance the effectiveness of CCTV-based threat analysis, the efficiency of CCTV systems and their impact on detecting and reducing criminal activities are discussed. The comparative outcomes of different detection approaches and their ensembles have been empirically discussed. © 2023 IEEE.

关键词： CCTV (Closed Circuit Television) CNN Deep learning Haar-Cascades image Classification image processing SVM (Support Vector Machine)

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Virtual Network Embedding Based on Space-Time Resource Tree for Space-Ground Integrated Network 3

Virtual Network Embedding Based on Space-Time Resource Tree ...

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3rd International conference on Signal image processing and Communication, ICSIPC 2023

作者： Qu, Hua Ren, Yayun Zhao, Jihong Wu, Siying Wang, Siyan Mao, Lin School of Software Engineering Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9781510670945

Space-Ground Integrated Network (SGIN) is a multi-domain integrated network with a large coverage area, which can better meet the ubiquitous communication needs in the network and provide users with better services. It has become the main development direction of 6G network. The different networks integrated in SGIN are heterogeneous, which is specifically reflected in the fact that the periodic movement of satellite networks brings time-varying nature to resources, and the expansion of network scale also brings spatial attributes to resources. It can be concluded that resources in SGIN have multi-dimensional. When performing virtual network embedding(VNE) in SGIN, the existing embedding algorithms often do not consider the multi-dimensionality of resources, which may lead to problems such as spatially dispersed embedding results and inability to adapt to network dynamic changes. To address this problem, this paper uses the space-time resource tree(S-TRT) model to represent the multi-dimensional resources in SGIN, reflecting the performance of resources in four dimensions: time, space, type, and quantity. On the basis of this model, combined with multiple dimensions of resources and the spatial distribution of embedded nodes, the ranking vector of virtual nodes is established, and the dimensionality reduction sorting of each virtual node is carried out by multi-dimensional scaling method. Afterwards, the node embedding is completed by dynamically sorting the physical nodes to improve the spatial concentration of the embedding results and better adapt to the dynamic changes of the underlying network. Finally, we conducted a simulation experiment on the algorithm, and the results show that the algorithm has good performance in the request acceptance rate and long-term revenue-to-cost ratio. © 2023 SPIE.

关键词： Network embeddings

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