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

共 15614 条记录，以下是111-120 订阅

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Research on the Design of Financial Institution Customer Identity Verification systems Based on Artificial Intelligence and Machine Recognition 2

Research on the Design of Financial Institution Customer Ide...

引用

2nd IEEE International conference on image processing and Computer Applications, ICIPCA 2024

作者： Shi, Jiayi Co. Ltd. Shanghai200127 China

ISBN: (纸本)9798350360240

Over the recent years, fraudulent activities in financial institutions have increased in China;according to criminals, there are several approaches to deceiving their users. This article focuses on applying Artificial Intelligence (AI) and Machine Learning in customer identity verification, a move driven towards recognizing and addressing the increasing bank fraud cases. The research introduces novel machine learning systems and models designed to determine fraudulent banking transactions. Various methods for enhancing detection accuracy are described. An artificial neural network-based model is proposed to enhance the system's efficiency and accuracy in identifying unauthorized transactions. The outcomes of variedly designed algorithms are, in this case, presented, typifying the impact of the logistic regression algorithm in enhancing the performance of the system, while stacked generalization demonstrates a higher AUC at 0.954. Recognizing bank fraud using artificial intelligence algorithms remains a significant concern in today's digital society. © 2024 IEEE.

关键词： Sales

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Copy-Move image Forgery Detection using Deep Learning Approaches 1

Copy-Move Image Forgery Detection using Deep Learning Approa...

引用

1st International conference on Software, systems and Information Technology, SSITCON 2024

作者： Ibrahim, Zainab Shawkat Hasan, Taha Mohammed University of Diyala College of Science Department of Computer Science Iraq

ISBN: (纸本)9798350352931

The advancement of digital image processing software has reached a stage where it is effortless to manufacture forgeries by using numerous manipulating approaches on authentic photos. Occupations such as law, healthcare, and education are susceptible to manipulated imagery. Therefore, it is crucial to ascertain the authenticity of an image. Single type of image manipulation is identified as 'copy move,' when significant item or objects are concealed by duplicating and inserting them into the original image. It is crucial to determine whether an image is genuine. The current techniques employed to identify image forgeries relied on conventional feature extraction algorithms, such as key point and block-based algorithms. These conventional approaches yield a subpar outcome. Deep learning approaches have established superior performance in image processing applications. This paper presents a highly effective method for identifying instances of copy-move forgery in images using a hybrid approach that combines deep learning (DL) such as (VGG19 and ResNet50) for feature extraction and Machine Learning (ML) algorithms including (Random Forests (RF) and Support Vector Machine (SVM)) to classify digital images. The proposed models (VGG19-RF), (VGG19-SVM), (ResNet50-RF), and (ResNet50-SVM) have been compared using two standard datasets. The results indicate that ((VGG19-RF) achieves higher recall, accuracy, precision and F1-score of 97% on MICC-F220 dataset, On the other hand (VGG19-RF) and (ResNet50-RF) achieve higher recall, precision, accuracy and F1-score of 97% on the MICC-F2000 dataset. © 2024 IEEE.

关键词： Support vector machines

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High-Dimensional Data processing Using Quantum-Inspired Evolutionary algorithms for Homeland Security Imaging systems

High-Dimensional Data Processing Using Quantum-Inspired Evol...

引用

2024 International conference on Communication, Computer Sciences and Engineering, IC3SE 2024

作者： Alekhya, V. Reddy, N. V. Uma Singh, Jay Boddu, Bhasker Sobti, Rajeev Hameed, Ali Abduhussien Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Artificial Intelligence And Machine Learning Bangalore India Gl Bajaj Institute Of Technology & Management U.P. Greater Noida India Mlr Institute Of Technology Department Of Information Technology Telangana Hyderabad India Lovely Professional University Phagwara India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

This research study explores the emerging area of quantum-inspired evolutionary algorithms (QIEAs) applied to high-dimensional data processing, with a focus on homeland security imaging systems. This work attempts to close the paradigm gap in image processing methodologies caused by the growing complexity of security threats. By combining the adaptive processes of evolutionary algorithms with the probabilistic reasoning inherent in quantum computing, the proposed technique creates a potent tool for managing the inherent difficulties of high-dimensional data. The main contribution of this study is the creation of a brand-new QIEA framework that is specifically designed for image analysis in security contexts and exhibits higher accuracy and efficiency than traditional techniques. The quantum bit representation and quantum gate operations, which have been cleverly tailored to the evolutionary algorithm structure, provide the methodological basis, and improve the search capacity in multidimensional data fields. An unparalleled degree of clarity and detail in security photography is made possible by this fusion, which is essential for threat identification and prevention. Experiments conducted on a variety of difficult datasets show that the suggested method is reliable for identifying important characteristics in complicated photos, which is a crucial component of homeland security applications. Beyond only addressing short-term security issues, this study establishes a standard for next investigations into quantum-inspired computing for image processing. © 2024 IEEE.

关键词： Imaging systems

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Segmentation-based low-resolution spatial tracker for video 34

Segmentation-based low-resolution spatial tracker for video

引用

conference on Automatic Target Recognition XXXIV

作者： Benyamin, Minas Goldman, Geoffrey H. DEVCOM Army Res Lab 2800 Powder Mill Rd Adelphi MD 20783 USA

ISBN: (纸本)9781510673977;9781510673960

Low-resolution image object recognition and tracking is often required for battlefield reconnaissance. For high-cost military systems, standard signal processing techniques can be used by tracking systems, however, low-cost systems require simpler approaches. We developed a fast detector-agnostic tracker for improving situational awareness using electro-optical video data. Our approach uses low computational techniques such as YOLO, match filters, and shape transforms to segment objects of interest in an image. From two or more successive detections, we initialize an alphabeta filter that predicts the location of the target of interest in the image. Next, we segment subsequent frames to a search area around the predicted region. This increases the sensitivity of the detector by improving the average signal-to-noise ratio and it also decreases the false alarm rate. The reduction in the size of the processing area can improve the detection speed per frame by an order of magnitude relative to a full-sized frame. By using algorithms with input from variablesize object a such as YOLO, this algorithm can be adapted to track virtually any object captured in a video.

关键词： Tracking Detection YOLO Hough Transform image processing Electro-optical Alpha-Beta Filter Low Resolution Classification

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Bayesian Optimization for Auto-tuning Convolution Neural Network on GPU 23rd

Bayesian Optimization for Auto-tuning Convolution Neural Net...

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23rd International conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Zhu, Huming Liu, Chendi Zhang, Lingyun Dong, Ximiao Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

ISBN: (纸本)9789819708109;9789819708116

GPU as a hardware processor plays an important role in the training of deep neural networks. However, when using GPUs for computation on convolutional neural network models, different combinations of GPU kernel configuration parameters have different performance. Therefore, this paper proposes BAGF, a bayesian auto-tuning framework for GPU kernels, which parameterizes the factors affecting the performance of GPU programs and uses bayesian optimization methods to search for the best parameters in the search space consisting of the parameters. Compared with other optimization algorithms, BAGF obtains excellent configuration parameters with fewer iterations. This paper analyzes the performance of BAGF on four benchmarks and compares with other common optimization algorithms. In addition, the performance improvement of each parameter configuration is analyzed. Finally, the BAGF was tested with the convolution layer of Alexnet, and the results of the Roofline model were analyzed. Compared with the original parameter configuration, the speed of BAGF was increased by 50.09%.

关键词： CNN Parallel computing GPU Auto-tuning

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Exploring the Potential of Deep Learning algorithms in Medical image processing: A Comprehensive Analysis 5th

Exploring the Potential of Deep Learning Algorithms in Medic...

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5th International conference on Communication and Intelligent systems, ICCIS 2023

作者： Pal, Ganesh Prasad Pal, Raju Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering School of Information and Communication Technology Gautam Buddha University Greater Noida India

ISBN: (纸本)9789819720781

In this study, we comprehensively examine the potential of deep learning algorithms in the domain of medical image processing. Through a systematic analysis of existing literature, we explore the applications, methodologies, and outcomes of these algorithms across diverse medical specialties. Our analysis underscores the transformative impact of convolutional neural networks and transfer learning techniques in enhancing diagnostic accuracy, image segmentation, and disease detection. We discuss challenges such as data variability, ethics, and interpretability while emphasizing the importance of interdisciplinary collaboration. By synthesizing findings and identifying future directions, we highlight the promising role of deep learning in revolutionizing healthcare diagnostics and treatment planning. After conducting a thorough analysis of various literature and empirical studies, we have evaluated the capabilities and drawbacks of deep learning models in managing different medical imaging modalities such as X-rays, MRIs, and CT scans. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COviD-19

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Estimating the Feasibility of images Simulation in Unity 3D for Sub-Pixel processing algorithms

Estimating the Feasibility of Images Simulation in Unity 3D ...

引用

2023 Wave Electronics and its Application in Information and Telecommunication systems, WECONF 2023

作者： Sivov, N. Yu. Shmatko, E.V. Poroykov, A. Yu. National Research University MPEI Department of Physics Named after V.A. Fabrikant Moscow Russia

ISBN: (纸本)9798350348293

Computer image simulation is used in the development of optical measurement systems and processing algorithms. It allows the performance of a system or algorithm to be verified and its measurement uncertainty to be assessed. Modern algorithms make it possible to achieve sub-pixel accuracy in digital image processing. An example of such algorithms is the cross-correlation processing in Particle image Velocimetry. Often, standard graphics engines such as Unity 3D and Unreal Engine are used to simulate the images. The purpose of this work is to evaluate the possibility of using these tools to generate images for further processing by subpixel resolution algorithms. This paper presents the results of a computational experiment to estimate the maximum spatial resolution for simulations in Unity 3D. A qualitative and quantitative analysis of the results confirms the possibility of using the tested graphics engine for the analysis of subpixel resolution algorithms for digital image processing. © 2023 IEEE.

关键词： Engines

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Design and Optimization of a Student Management System Based on Blockchain Technology in District Computers 2

Design and Optimization of a Student Management System Based...

引用

2nd IEEE International conference on image processing and Computer Applications, ICIPCA 2024

作者： Jin, Ying Liaoning Fushun113122 China

ISBN: (纸本)9798350360240

Computer blockchain technology provides a possible means to enhance data security, transparency, and overall efficiency. This study focuses on the design and optimization ideas of using blockchain technology in student management systems. The architecture plan for this system was developed during the design phase, emphasizing the use of smart contracts to automate registration, classification, and authentication processes, as well as decentralized data storage and encrypted hashing of irreversible records. License blockchain can be used to provide privacy protection and access restrictions, ensuring that student data is secure and accessible to authorized parties. The goal of optimizing the solution is to improve scalability and transaction speed, while reducing the amount of energy used in traditional blockchain systems. The improvement of consensus algorithms and second layer extension technologies such as sidechain or off chain protocols can achieve faster data processing without compromising security. In addition, metadata management and advanced encryption technologies have improved system performance and minimized data to the greatest extent possible. © 2024 IEEE.

关键词： Blockchain Technology Computer Decentralized Data Storage Educational systems Management systems Privacy Protection

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Hardware Implementation of image processing Morphological and Convolution Operations as SoC on FPGAs 24

Hardware Implementation of Image Processing Morphological an...

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7th International conference on Digital Medicine and image processing, DMIP 2024

作者： Moussa, Alfred M. Groves, Richard Rafla, Nader Electrical and Computer Engineering Department Boise State University BoiseID United States

ISBN: (纸本)9798400709586

Efficient image processing architectures are consistently in demand across a multitude of applications, particularly those customized for resource-constrained systems-on-chip (SoC). The increasing need for high-performance image processing in various sectors has driven the development of specialized architectures. However, deploying such architectures on platforms with limited resources, such as SoCs, poses significant challenges. Furthermore, the implementation of complex algorithms to handle large datasets using software solutions often leads to slower response times, prompting exploration into hardware implementations. Field-Programmable Gate Arrays (FPGAs) are becoming popular for hardware implementations because of their attributes: low latency, connectivity, parallel computing capabilities, and flexibility. Consequently, the utilization of FPGA-based implementations has resulted in faster and more efficient performance of unique architectures tailored to specific requirements. This paper presents a novel hardware/software co-design approach to implement erosion, dilation, and neighborhood image processing operations on the FPGA development board, "Zedboard". In this approach, the FPGA is programmed by connecting it to a PC via USB, facilitating the transfer of an image pixel by pixel. The pixels are temporarily stored in on-chip DDR and accessed through DMA (Direct Memory Access) until they are requested by an interrupt signal from the image processing IP, at which point they are moved to line buffers for faster processing. Once processed, the image is transmitted back to the PC via UART, facilitating pixel-by-pixel transfer for verification, where it is compared with a reference image generated using Python. This comparison confirms a 99.22% match between the processed image and the reference image, with the discrepancy occurring at the image's edges due to initial padding. Additionally, the time required to process the entire image was measured and displayed

关键词： System-on-chip

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Maritime image Stabilization: A Comprehensive Review of Techniques and Challenges 9

Maritime Image Stabilization: A Comprehensive Review of Tech...

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9th International conference on Electronic Technology and Information Science (ICETIS)

作者： Wei, Enping Tan, Yong Chai Tai, vin Cent Hao, Yanan Zhang, Xiaodong Zhang, Tian SEGI Univ Fac Engn Built Environm & Informat Technol Ctr Modelling & Simulat Kuala Lumpur Malaysia

ISBN: (纸本)9798350388350;9798350388343

image stabilization plays a crucial role in providing accurate and reliable visual information for machine vision applications. In maritime applications, such as unmanned ship navigation, where six degrees of freedom (DOF) motion and harsh maritime conditions prevail, the efficacy of image stabilization technology is vital for robust image processing algorithms. This paper offers a comprehensive review of image stabilization techniques tailored for maritime environments, developed over the past two decades. We analyzed a total of 39 research articles on the subject, sourced from Web-of-Science, SCOPUS, and the Engineering Index databases, discussing potential research directions to address the limitations of current image stabilization methods, with special consideration for the unique requirements of ship-borne cameras. It provides an up-to-date overview of the techniques, limitations, and algorithms of ship-borne cameras for maritime applications, identifying current knowledge gaps and areas requiring further research. This review aims to guide the development of new technologies and methods to improve the performance of image stabilization systems in maritime contexts.

关键词： image Stabilization Assessment Application Maritime Environment Ship-borne Camera

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