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

共 16349 条记录，以下是141-150 订阅

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Design of Axi Bus-Based IP Core for image processing 12th

Design of AXI Bus-Based IP Core for Image Processing

引用

12th CCF conference on BigData, BigData 2024

作者： Chen, Yutong Yi, Zhongchao Li, Xuqiang Chen, xingyan Chen, Yanjiang School of Software Engineering University of Science and Technology Hefei China School of Computer Science and Technology University of Science and Technology Hefei China

ISBN: (纸本)9789819610235

This study addresses the significant issue of image blur through image restoration techniques that reduce or remove blur and detect blurry regions, improving image quality and analysis potential. Utilizing FPGA-based digital hardware systems, the research leverages FPGA-supported systems like ZYNQ and ARM, using the Advanced eXtensible Interface (Axi) and Advanced Microcontroller Bus Architecture (AMBA) for streamlined device communication. The design incorporates specialized Intellectual Property (IP) cores such as Square Root (Sqrt) and Shift RAM, tailored to overcome the limitations of development cycles and processing algorithms, focusing on portability across various platforms including domestic FPGAs. The practical implementation features modules for axis-stream transmission, RGB to YUV conversion, image smoothing using Gaussian, mean, and median filters, advanced image processing with Sobel edge detection, and erosion-dilation modules. A ZYNQ verification system with an OV5640 camera and HDMI output substantiates the functionality and efficiency of the designed IP core, which is further analyzed for resource and power consumption using a High-Level Synthesis (HLS) tool. The study successfully demonstrates a robust IP core for blurry image processing, significantly enhancing the field of image processing and embedded systems, laying a strong groundwork for future advancements and porting endeavors, and highlighting the efficacy of FPGA-based image processing solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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Fpga-Based Accelerator for Convolutional Neural Networks

Fpga-Based Accelerator for Convolutional Neural Networks

引用

Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： xi Cheng School of Physics and Technology Wuhan University Wuhan China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Convolutional neural networks (CNNs) have emerged as a pivotal deep learning architecture within the realms of artificial intelligence and machine learning, finding extensive application in image recognition, speech processing, and numerous other domains. As the CNN model continues to evolve and meet with growing application demands, it inevitably entails heightened computational complexity and resource consumption, thereby necessitating advanced hardware accelerators. Currently, the design of CNN accelerators leveraging field-programmable gate arrays (FPGAs) has garnered significant attention in research circles. FPGAs offer notable advantages such as reduced power consumption and exceptional flexibility, making them an ideal candidate for hardware acceleration tasks. This paper undertakes a comprehensive review of the advancements in CNN accelerator designs utilizing FPGA technology. Researchers have concentrated their efforts on enhancing accelerator performance through strategic optimizations in bandwidth utilization and the development of highly efficient computing engines. To further boost performance, they have employed space exploration algorithms, introduced multiple state machine designs, and devised a sophisticated deep pipeline architecture to facilitate seamless interconnection among multiple FPGA boards. These collective optimization strategies have proven instrumental in dramatically elevating both the computational efficiency and energy efficiency of FPGA-based CNN accelerators.

关键词： Computational modeling Bandwidth Parallel processing Energy efficiency Convolutional neural networks Resource management Field programmable gate arrays Optimization Hardware acceleration Engines

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MSE-based Sampling of Bandlimited Product Graph Signals via Joint Low-pass Impulse Responses

MSE-based Sampling of Bandlimited Product Graph Signals via ...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Fen Wang Baoyi Xu Xuyao Kang Peng Ren Long Yang Hangzhou Institute of Technology Xidian University Hangzhou China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Matrix graph signals, which are associated with two factor graphs, are ubiquitous in daily life, such as time-varying physical signals in sensor networks and rating matrices in recommendation systems. In practice, due to the row-wise and column-wise smoothness, they are modeled as bandlimited (BL) graph signals on the product of two factor graphs. In this paper, we propose a fast sampling method for BL product graph signals by minimizing the reconstruction mean squared error (MSE). First, we formulate the MSE-based sampling objective for BL signals on a product graph. Next, we simplify the greedy subproblem using matrix inversion approximation, which involves straightforward computations between the defined joint low-pass impulse responses (JLIRs). To eliminate the huge computation and storage complexity, we propose a strategy to directly compute the greedy evaluation score based on low-dimensional LIRs on two factor graphs, leveraging the property of Kronecker product. Employing this strategy, we also extend existing popular LIR-based methods to sample signals from single graphs to product graphs with low complexity. Extensive experiments on synthetic and real-world product graph signals demonstrate that our proposed method maintains superior MSE performance compared to other extended graph sampling schemes.

关键词： Signal processing algorithms Signal processing Sampling methods Approximation algorithms Acoustics Complexity theory Speech processing Time-varying systems Recommender systems

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On Momentum Acceleration for Randomized Coordinate Descent in Matrix Completion

On Momentum Acceleration for Randomized Coordinate Descent i...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Matthew Callahan Trung Vu Raviv Raich School of EECS Oregon State University Corvallis OR USA Department of CSEE University of Maryland Baltimore County MD USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Matrix completion plays an important role in machine learning and signal processing, with applications ranging from recommender systems to image inpainting. Many approaches have been considered to solve the problem and some offer computationally efficient solutions. In particular, a highly-efficient random coordinate descent approach reduces the per-epoch computation dramatically. This paper is concerned with further improvement of computational efficiency to expand the range of problem sizes and conditions that can be solved. Momentum acceleration is a well-known method to improve the efficiency of iterative algorithms, but applying it to random coordinate descent methods without increasing the computational complexity is non-trivial. To address this challenge, we introduce a momentum-accelerated randomized coordinate descent for matrix completion approach that does not increase computational complexity by accelerating at the level of epochs. Additionally, we propose an analysis-driven, tuning-free method for step size selection. To that end, we offer a convergence rate analysis for the algorithm. Using numerical evaluations, we demonstrate the competitiveness of the method and verify the theoretical analysis.

关键词： Closed-form solutions Shape Signal processing algorithms Machine learning Signal processing Computational efficiency Computational complexity Speech processing Recommender systems Convergence

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Learning to Follow Infrared Prior Repersentation for image Dehazing

Learning to Follow Infrared Prior Repersentation for Image D...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yiquan Du Ming Yang Haiyang Huo Jiaqi Shi Jiangang Ding Lili Pei School of Information Engineering Chang’an University Xi’an China School of Data Science and Artificial Intelligence Chang’an University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The infrared image can distinguish the targets from the background based on radiation differences, providing more significant target visibility under dense haze. Fusion of visible haze images with infrared prior representations can generate high-quality fused images for high-level tasks. Consequently, we propose a novel dual-modal fusion network structure that makes full use of infrared prior representations for dehazing. Specifically, we emphasize a Multi-modal Feature Extraction Network (MMFE) to extract deep multi-scale features. Meanwhile, we introduce a Multi-scale Feature Extraction module (MSFE), integrating an Efficient Dual Attenion block (EDAB) to efficiently explore more spatial and marginal information. Additionally, we propose a new feature fusion strategy, which calculates feature fusion weights based on an adaptive multi-head self-attention. Therefore, IPRDehazeNet achieves better dehazing results through dual-modal fusion. Experimental results indicate that IPRDehazeNet outperforms various advanced methods.

关键词： Adaptive systems Fuses Signal processing Feature extraction Acoustics Speech processing

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Comprehensive image Classification and Object Detection Using Convolutional Neural Networks

Comprehensive Image Classification and Object Detection Usin...

引用

Disruptive Technologies (ICDT), International conference on

作者： Archana Kumari Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In the modern era, Deep Learning (DL) methodology is used extensively in multiple areas and Convolutional Neural Network (CNN) is majorly used for addressing the problems in real-time for computer vision activities due to its capability of enabling acquisitions with higher accuracy. The concept at the back of this DL technique was inspired from architecture of biotic visual systems. In the current time, it is applied extensively in multiple areas and industries like automotive, medical and healthcare, space, aviation, agriculture etc. Moving forward, numerous algorithms on CNN were proposed to address the requirement of multiple areas, with Natural Language processing (NLP) and computer vision as the major fields for application. The activities in computer vision with major application of CNN includes image categorization, object identification, recognition of face, estimation of human pose, recognition of action, scene labelling along with document analysis. This paper delves into modern techniques of CNN for classification of images and detection of objects.

关键词： Deep learning Computer vision image recognition Face recognition Medical services Real-time systems Natural language processing Agriculture Convolutional neural networks Biomedical imaging

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Research on image brightness enhancement algorithm based on deep learning 24

Research on image brightness enhancement algorithm based on ...

引用

Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Siqi He Jianping xing Yu Bai Shuzhen Huang Kaiping Sun Peng Sun Shandong University Jinan Shandong Province China Shandong Vocational and Technical University of Engineering Jinan Shandong Province China

ISBN: (纸本)9798400713880

In view of traditional image improvement technology's limitations in high real-time application environments, a new low-light video image improvement algorithm is proposed in this article. In consideration of traditional improvement algorithms' faults, and under the precondition of studying low-light improvement techniques in image signal processing (ISP) systems, deep learning frameworks are introduced to break through complex structure and long processing pipeline faults in traditional ISP systems. In this article, a lightweight image brightness improvement algorithm with a basis in convolutional neural network (CNN) is proposed. In this algorithm, feature extraction of RGB images is performed through CNN to generate a brightness coefficient matrix, simplifying RGB to RGB direct mapping and reducing model numbers, model parameters, and computational complexity. This technology pays attention to generating coefficients of brightness, can effectively extract changing parts of brightness, maintain detail information, and conduct efficient and natural brightness improvement, and can apply to high real-time application environments such as autonomous driving.

关键词： Convolutional Neural Network

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Development of a Model for Texture Analysis of Machined Parts on CNC Machines

Development of a Model for Texture Analysis of Machined Part...

引用

International Russian Smart Industry conference (SmartIndustryCon)

作者： Ilya Kovalev David Malykh Alexander Kiselev department of Computer Control Systems MSUT “STANKIN” Moscow Russia department of Robotics and Mechatronics MSUT “STANKIN” Moscow Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This work is devoted to the development of a software module for analyzing the textures of machined parts on CNC machines using computer vision methods. The main goal of the research is to create a tool capable of analyzing geometric parameters of products based on texture properties of materials, which is especially relevant in the conditions of high-volume production. The paper considers modern methods of texture analysis, as well as their robustness to various disturbances such as illumination changes, noise, blurring, rotation and image scaling. An analysis system that combines binocular and texture methods of image processing is proposed, which makes it possible to improve the accuracy of measuring distances to objects on the basis of binocular algorithm, by means of additional analysis of texture properties of surfaces of the processed part.

关键词： Computer vision image analysis Software algorithms Noise User interfaces Software Robustness Classification algorithms Surface texture Surface treatment

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Identifying Adversarial Attacks in Crowdsourcing via Dense Subgraph Detection

Identifying Adversarial Attacks in Crowdsourcing via Dense S...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Abdullah Karaaslanli Panagiotis A. Traganitis Aritra Konar Dept. of Electrical & Computer Engineering Michigan State University East Lansing MI USA Dept. of Electrical Engineering KU Leuven Leuven Belgium

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Crowdsourcing is becoming increasingly important for contemporary applications in machine learning and artificial intelligence. However, crowdsourcing systems may be susceptible to adversarial attacks where a subset of annotators deliberately provide erroneous responses. This paper introduces a novel algorithm for identifying adversarial attacks in crowdsourcing systems by recasting the problem as dense subgraph detection in bipartite graphs. In particular, we represent crowdsourced data as a weighted bipartite graph between workers and data points which are connected with edges whose weights are computed from annotators’ responses. The constructed bipartite graph is then analyzed with a sequential peeling algorithm to detect the dense subgraph that includes adversarial attacks. Compared to previous methods where only adversaries are detected, our proposed method can simultaneously identify adversarial annotators as well as affected data points. Preliminary results on real datasets showcase the potential of this novel approach.

关键词： Crowdsourcing Machine learning algorithms image edge detection Signal processing algorithms Machine learning Signal processing Acoustics Bipartite graph Speech processing

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Real-Time Dynamic image Stitching Using GNN-Based Feature Matching

Real-Time Dynamic Image Stitching Using GNN-Based Feature Ma...

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Mechatronics and Robotics Engineering (ICMRE), International conference on

作者： Young Hoon Suh Eun Ho Kim Yeong Gwang Choi JaeWook Jeon Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Especially in static situations, image stitching has been routinely used to seamlessly merge many photographs into a panorama. Using it in dynamic and real-time environments, however, presents a number of difficulties, such as managing shifting objects, changing surroundings, and processing limitations. An image stitching look at that works well in dynamic real-time situations is presented in this paper. The method makes use of GNN-based feature matching, dynamic object removal, and real-time synthesis techniques. Comparative studies suggest that, in comparison to traditional methods like SIFT and ORB, the suggested approach meets real-time processing requirements while achieving greater registration precision, lower homography RMSE, and consistent panoramic outputs. The findings imply that the algorithm might be used in fields where precision and efficiency are required in dynamic circumstances, like parking management systems, sports broadcasting, and autonomous driving.

关键词： Accuracy Smoothing methods Heuristic algorithms Surveillance Real-time systems Robustness image stitching Robots Optimization Sports

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