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检索条件"任意字段=Parallel and Distributed Methods for Image Processing II"

共 1263 条记录，以下是101-110 订阅

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A Noise-Based Approach Augmented with Neural Cleanse and JPEG Compression to Counter Adversarial Attacks on image Classification Systems 33

A Noise-Based Approach Augmented with Neural Cleanse and JPE...

引用

33rd Euromicro International Conference on parallel, distributed, and Network-Based processing, PDP 2025

作者： Kotenko, Igor Saenko, Igor Lauta, Oleg Vasiliev, Nikita Sadovnikov, Vladimir Laboratory of Computer Security Problems Saint-Petersburg Russia Admiral Makarov State University of Maritime and Inland Shipping Department of Telecommunications and Information Security Saint-Petersburg Russia

ISBN: (纸本)9798331524937

Adversarial attacks are now becoming quite a dangerous means of disrupting image processing systems that use machine learning methods for decision making. Therefore, developing effective countermeasures against adversarial attacks is becoming quite an important area of cybersecurity. The paper proposes a noise-based approach to countering adversarial attacks that is augmented with neural-cleanse and jpeg-compression technologies. The idea of the proposed approach is that adding noise distorts the effect of an adversarial attack, and neural cleaning and jpeg compression eliminate the consequences of such an effect. The paper examines the three most well-known types of adversarial attacks: Fast Gradient Sign Method, Zeroth Order Optimization and One Pixel Attack. These attacks manipulate input data, resulting in misclassification or incorrect predictions by exploiting high-frequency components that are undetectable to humans. The research was carried out on two datasets: MNIST-JPG and PC Parts images. Two types of noise were used: Gaussian and Poisson. During the experiments, optimal parameters for these types of noise were found, ensuring maximum accuracy of image recognition after exposure to adversarial attacks. © 2025 IEEE.

关键词： Disinfection

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Design and Implementation of a Workflow-Based Architecture for distributed Medical image AI Systems

Design and Implementation of a Workflow-Based Architecture f...

引用

IEEE International Conference on e-Business Engineering (ICEBE)

作者： Yu Ge Qi Zhang Yibin Chen Kuo-Ming Chao Pan Hu Hongming Cai School of Software Shanghai Jiao Tong University Shanghai China Computing Department University of Roehampton London United Kingdom

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

Medical image AI Systems can assist doctors in making diagnoses, thereby improving diagnostic accuracy. These systems are now widely used in hospitals. However, current AI diagnostic methods typically rely on various deep learning technologies, which require substantial computational resources. When diagnostic demands surge, traditional monolithic architectures may suffer from low computational performance and queue congestion. To address these issues, this paper compares and analyzes two system architectures based on parallel computing and distributed computing. The first one is a coarse-grained multi-service instance architecture, which uses clustering to expand the system's service instances, though it still presents some challenges. The second is a fine-grained workflow-based distributed architecture, which abstracts the diagnostic process into a workflow divided into several subtasks managed and scheduled by the cluster. This architecture demonstrates advantages in several aspects. Finally, this paper implements a Medical image AI System for pulmonary fibrosis diagnosis based on the workflow-based distributed system architecture.

关键词： Deep learning Hospitals Lungs Systems architecture Computer architecture parallel processing Artificial intelligence distributed computing Surges Medical diagnostic imaging

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A Comparative Study of Transfer Learning Approaches For Medical image Analysis

A Comparative Study of Transfer Learning Approaches For Medi...

引用

International Conference on parallel distributed and Grid Computing (PDGC)

作者： Prince Priya Malla Sudhakar Sahu School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar Odisha India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Deep learning based approaches have been extensively used for the image classification task with improved accuracy and lower computational complexity. The deep learning approaches have shown outstanding performances when a huge amount of datasets are available to train the model. However, in medical image analysis, sufficient data is not available to train deep learning models. To overcome these issues, a new approach called transfer learning has been introduced for the medical image classification task. This approach has shown outstanding performances with less computational complexity. In the case of transfer learning approaches, the knowledge acquired by training the model with a non-medical image dataset can be transferred to the target dataset. This paper presents a comparative study on the application of transfer learning techniques for the medical image analysis task. This paper demonstrated a complete analysis of the architecture and performance of numerous pre-trained models on the imageNet dataset. Further, This paper presents the significance of the transfer learning techniques for medical image processing tasks for example brain tumor detection, pneumonia classification, and COVID-19 detection by analyzing various state-of-the-art methods. The experimental results indicate that the performance of transfer learning-based methods can be improved by fine-tuning these models using medical-specific datasets.

关键词： Deep learning Analytical models Accuracy Computational modeling Transfer learning Brain modeling Medical diagnosis Computational complexity Medical diagnostic imaging image classification

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QRELATION: AN AGENT RELATION-BASED APPROACH FOR MULTI-AGENT REINFORCEMENT LEARNING VALUE FUNCTION FACTORIZATION 47

QRELATION: AN AGENT RELATION-BASED APPROACH FOR MULTI-AGENT ...

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47th IEEE International Conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Shen, Siqi Liu, Jun Qiu, Mengwei Liu, Weiquan Wang, Cheng Fu, Yongquan Wang, Qinglin Qiao, Peng Xiamen Univ Sch Informat Fujian Key Lab Sensing & Comp Smart Cities Xiamen Peoples R China Natl Univ Def Technol Parallel & Distributed Proc Lab Changsha Peoples R China

ISBN: (纸本)9781665405409

The Centralized Training with Decentralized Execution paradigm (CTDE), which trains policies centrally with additional information, is important for Multi-Agent Reinforcement Learning (MARL). For CTDE, value function factorization methods make use of state during training and factorize the value function into multiple local value functions for decentralized execution. These approaches do not fully consider the relational information among agents, resulting in sub-optimal models for complex tasks. To remedy this issue, we propose QRelation which is a graph neural network approach for value function factorization. It considers both the static relations (e.g., agent types) and dynamic relations (e.g., close-by). We show that QRelation can obtain better results than state-of-the-art methods on challenging StarCraft ii benchmarks.

关键词： Training Conferences Reinforcement learning Benchmark testing Signal processing Graph neural networks Acoustics

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Frequency-Domain Guided Multiple parallel Kernels Network for Low-Light Remote Sensing image Enhancement

Frequency-Domain Guided Multiple Parallel Kernels Network fo...

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

作者： Jingxuan Zhou Hao Li Jinlong Wang Xiongxin Tang Fanjiang Xu Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to dark environments, optical aberrations, etc, the remote sensing images are often submerged under low contrast degradation, which greatly hinders their practical applications for agricultural management and other related tasks. The surface features of remote sensing images are often continuously distributed in space, thus, the sizes of the network’s receptive fields and its ability to learn long-range dependencies are crucial for restoring low-light remote sensing images. Existing methods based on CNN provide limited receptive fields, while Transformer-based methods are constrained by their quadratic computational complexity. To cope with these issues, we propose a novel low-light remote sensing image enhancement network that combines multi-scale receptive fields with frequency-domain attention. Specifically, this network employs multiple parallel kernels of varying sizes to learn multi-scale local features in the spatial domain and complements frequency-domain information to learn global long-range correlations, which achieves local-global feature extraction and further facilitates subsequent degraded images enhancement. We have conducted extensive experiments to demonstrate that our network outperforms existing methods quantitatively and achieves exceptional visual performance, which fully highlights the effectiveness and superiority of our method in enhancing low-light remote sensing images.

关键词： Visualization Frequency-domain analysis Transforms Speech enhancement Feature extraction Transformers image restoration Kernel Remote sensing image enhancement

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Optimizing Transitive Closure Computation for High Performance Computing and Security 33

Optimizing Transitive Closure Computation for High Performan...

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33rd Euromicro International Conference on parallel, distributed, and Network-Based processing, PDP 2025

作者： Masciari, Elio Napolitano, Enea Vincenzo DIETI University of Naples Federico II Naples Italy

ISBN: (纸本)9798331524937

Transitive closure computation is a fundamental operation in graph theory with applications in various domains. However, the increasing size and complexity of real-world graphs make traditional algorithms inefficient, especially when dealing with large datasets. This paper investigates the optimisation of transitive closure algorithms for high performance computing (HPC) applications. We implement and compare three different methods for computing the adjacency matrix of the transitive closure, based on three different Python libraries (NetworkX, PyTorch and NumPy). Our approach is benchmarked on seven real-world datasets of varying size and density to evaluate performance and scalability. The results show that NumPy achieves the best performance for large and dense graphs. The paper concludes with a discussion of the potential benefits of algorithmic optimization in HPC and security. © 2025 IEEE.

关键词： Graph algorithms

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MAP image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model

MAP Image Recovery with Guarantees using Locally Convex Mult...

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

作者： Jyothi Rikhab Chand Mathews Jacob Department of Electrical and Computer Engineering University of Iowa IA USA Department of Electrical and Computer Engineering University of Virginia VA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We propose a multi-scale deep energy model that is strongly convex in the local neighbourhood around the data manifold to represent its probability density, with application in inverse problems. In particular, we represent the negative log-prior as a multi-scale energy model parameterized by a Convolutional Neural Network (CNN). We restrict the gradient of the CNN to be locally monotone, which constrains the model as a Locally Convex Multi-Scale Energy (LC-MuSE). We use the learned energy model in image-based inverse problems, where the formulation offers several desirable properties: i) uniqueness of the solution, ii) convergence guarantees to a minimum of the inverse problem, and iii) robustness to input perturbations. In the context of parallel Magnetic Resonance (MR) image reconstruction, we show that the proposed method performs better than the state-of-the-art convex regularizers, while the performance is comparable to plug-and-play regularizers and end-to-end trained methods.

关键词： Manifolds Inverse problems Perturbation methods Magnetic resonance Signal processing Robustness Data models Convolutional neural networks Speech processing image reconstruction

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MiDedup: A Restore-Friendly Deduplication Method on Docker image Storage Systems 20th

MiDedup: A Restore-Friendly Deduplication Method on Docker ...

引用

20th IFIP WG 10.3 International Conference on Network and parallel Computing, NPC 2024

作者： Qin, Lisha Tan, Haoliang Zou, Xiangyu Ou, Wenhao Wei, Yan Huang, Rubing Xia, Wen Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China Zhongruan International Technology Service Co. Ltd. Shenzhen China Macau University of Science and Technology Macao China

ISBN: (纸本)9789819628292

Docker image storage systems, like Docker registry, often employ deduplication to reduce storage overhead. Existing deduplication methods for these systems detect redundancy at either layer or file level, each with its own advantages and limitations. Layer-level deduplication offers much faster restoration but lower deduplication ratio, as it eliminates duplicate layers while ignoring duplicate files across layers. Conversely, file-level deduplication offers a higher deduplication ratio but slower restoration, as removing duplicate files scatters the files of a layer, leading to multiple disk read I/O during restoration. Our analysis reveals that the slow restoration in file-level deduplication partly stems from ignoring file-layer relationships. In this paper, we propose MiDedup, a new Docker deduplication method. MiDedup detects redundancy at file-level and organizes files into a restore-friendly layout by considering file-layer relationships to enhance restore speed. Our studies also show that duplicated files of one layer are not uniformly distributed across different layers. MiDedup uses this insight to skip deduplicating scattered files to further optimize the file layout for faster restoration. Evaluations indicate that MiDedup achieves restore speeds that are 8–19× faster than file-level deduplication (slightly slower than layer-level one), while maintaining 9–47% higher deduplication ratios than layer-level deduplication. © IFIP International Federation for Information processing 2025.

关键词： Redundancy

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A Hybrid Multi-Modal Medical image Fusion Technique using Optimal Sub-band Tree Structuring and Principal Component Analysis

A Hybrid Multi-Modal Medical Image Fusion Technique using Op...

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International Conference on parallel distributed and Grid Computing (PDGC)

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In image processing, "image fusion" is the amalgamation of attributes and requirements from many images into a singular, more comprehensive representation. Multi-modal medical image fusion is a significant category of image fusion. It entails the integration of medical images acquired from several modalities. This work utilizes computed tomography (CT) scans, Positron Emission Tomography (PET) and magnetic resonance imaging (MRI) as modalities. This study (M3IF-SBTS) aims to construct a multi modal medical image approach that combines Optimal Sub-band Tree Structuring (SBTS) and Principal Component Analysis (PCA) with MRI and CT images in a manner that maximizes the information content in the fused image. The SBTS is an advanced wavelet transform version of Discrete Wavelet Transform (DWT), where signal is filtered more times. The PCA provides dimensionality reduction and retains the relevant features. The wavelet coefficients are fused using PCA-based fusion method. This combination of SBTS and PCA provides superior results compared to using SBTS, DWT, or PCA as individual methods. This improves overall visual and parametric quality of fusion results than many compared methods.

关键词： Visualization Magnetic resonance imaging Computed tomography Information filters Discrete wavelet transforms Positron emission tomography image fusion Principal component analysis Biomedical imaging Wavelet coefficients

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Attention Based Generative Adversarial Networks to Augment image Data for Rapid Training

Attention Based Generative Adversarial Networks to Augment I...

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International Conference on parallel distributed and Grid Computing (PDGC)

作者： Vijayakumar Adaickalam Sadagopan Selvarasu Gowthul Alam M A Suresh Kumar S Annamalai Sangeetha N School of Computer Science and Engineering and Information Presidency University Bangalore Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai School of Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Department of AI & ML City Engineering College Bangalore

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In this paper, we develop an Attention based Generative Adversarial Networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enables generation of images from the sample images that helps in training the classifier and in this study a fundamental classifier namely a convolutional neural network is used. A robust training is conducted to test the accuracy of detecting the instances effectively using the dataset. The simulation shows that the proposed AGAN-CNN attains improved accuracy after robust training than the existing methods.

关键词： Training Accuracy Training data Imaging Production Computer architecture Generative adversarial networks Grid computing Convolutional neural networks Hyperspectral imaging

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