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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing III"

共 552 条记录，以下是51-60 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

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

引用

Euromicro conference on parallel, distributed and Network-Based processing

作者： Igor Kotenko Igor Saenko Oleg Lauta Nikita Vasiliev Vladimir Sadovnikov Laboratory of Computer Security Problems St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) Saint-Petersburg Russia Department of Telecommunications and Information Security Admiral Makarov State University of Maritime and Inland Shipping Saint-Petersburg Russia

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

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.

关键词： Resistance image recognition image coding image resolution Noise Transform coding Machine learning Distortion Optimization image classification

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GPU Optimization of Biological Macromolecule Multi-tilt Electron Tomography Reconstruction Algorithm 1

引用

19th International conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Fu, Zi-Ang Wan, Xiaohua Zhang, Fa Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China

ISBN: (数字)9789819947492

ISBN: (纸本)9789819947485;9789819947492

Three-dimensional (3D) reconstruction in cryo-electron tomography (cryo-ET) plays an important role in studying in situ biological macromolecular structures at the nanometer level. Owing to limited tilt angle, 3D reconstruction of cryo-ET always suffers from a "missing wedge" problem which causes severe accuracy degradation. Multi-tilt reconstruction is an effective method to reduce artifacts and suppress the effect of the missing wedge. As the number of tilt series increases, large size data causes high computation and huge memory overhead. Limited by the memory, multi-tilt reconstruction cannot be performed in parallel on GPUs, especially when the image size reaches 1 K, 2 K, or even larger. To optimize large-scale multi-tilt reconstruction of cryo-ET, we propose a newGPU-based large-scale multi-tilt tomographic reconstruction algorithm (GMSIRT). Furthermore, we design a two-level data partition strategy in GM-SIRT to greatly reduce the memory required in the whole reconstructing process. Experimental results show that the performance of the GM-SIRT algorithm has been significantly improved compared with DM-SIRT, the distributed multi-tilt reconstruction algorithm on the CPU cluster. The acceleration ratio is over 300%, and the memory requirement only decreases to one-third of DM-SIRT when the image size reaches 2 K.

关键词： Graphics processing unit

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Efficient and parallel Separable Dictionary Learning 27

Efficient and Parallel Separable Dictionary Learning

引用

27th IEEE International conference on parallel and distributed Systems, ICPADS 2021

作者： Rusu, Cristian Irofti, Paul University Politehnica Bucharest Faculty of Automatic Control and Computers Department of Automatic Control and Computers Bucharest Romania University of Bucharest Faculty of Mathematics and Computer Science Department of Computer Science Bucharest Romania

ISBN: (纸本)9781665408783

Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images. In this paper, we describe a highly parallelizable algorithm that learns such dictionaries which reaches sparse representations competitive with the previous state of the art dictionary learning algorithms from the literature but at a lower computational cost. We highlight the performance of the proposed method to sparsely represent image and hyperspectral data, and for image denoising. © 2021 IEEE.

关键词： image denoising

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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...

引用

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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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 Normalized Mutual Information-based Multi-Modal Medical image Fusion Technique using Non Sub-sampled Shearlet Transform and Method Noise

A Normalized Mutual Information-based Multi-Modal Medical Im...

引用

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 process of merging characteristics and information from multiple images to build a single, more complete representation. This approach includes multi-modal medical image fusion, which involves fusing medical images from several imaging modalities such as computed tomography (CT) scans, Positron Emission Tomography (PET) and magnetic resonance imaging (MRI). This integration improves diagnostic accuracy by using each modality's own strengths, resulting in clearer visualization of anatomical structures and clinical situations. This work utilizes CT, PET, and MRI images as modalities for the experimentation purpose. This study (M3IF-NSST-MI) aims to construct a multi modal medical image approach that combines Non Sub-sampled Shearlet Transform (NSST) and method noise to the information content in the fused image, where high pass subbands are fused using Normalized Mutual Information (NMI) based fusion strategy. NSST domain is used for its Multi-Scale Representation and shift invariance. The concept of method noise as post-processing operation that extracts the information which was missed in the initial phase. The wavelet coefficients are fused using NMI-based fusion method. This combination of NSST with NMI-based fusion and method noise provides superior results than many conventional methods. This improves overall visual and parametric quality of fusion results than many compared methods.

关键词： Visualization Accuracy Magnetic resonance imaging Computed tomography Noise Positron emission tomography Medical diagnostic imaging image fusion Mutual information Wavelet coefficients

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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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Scalable SubXPCA on a distributed Platform Applied to Classification of Hyperspectral images

Scalable SubXPCA on a Distributed Platform Applied to Classi...

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conference on Information and Communication Technology (CICT)

作者： Bogolu Rupa Atul Negi School of Computer and Information Sciences University of Hyderabad Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

The exponential growth of technological advancements in satellite and airborne remote sensing is giving rise to large volumes of high-dimensional hyperspectral image data. Apache Spark is one of the most popular, extensively used and open-source distributed processing frameworks, which is proven effective in processing large volumes of remotely sensed hyperspectral images in a time-efficient manner. Open-source distributed processing frameworks have proven effective in processing large volumes of remotely sensed hyperspectral images quickly and efficiently. While computational power has been increasing, the rate of data accumulation is more than the processing capabilities. Therefore, more efficient algorithms such as dimensionality reduction are needed to process and get accurate performance for the application. This paper proposes an efficient and parallel spectral dimensionality reduction approach based on feature partitioning principal component analysis called scalable SubXPCA. We implemented scalable SubXPCA on a spark cluster distributed environment. We compared scalable SubXPCA against other distributed feature partitioning and various non-feature partitioning dimensionality reduction methods. Our experiments on different real and synthetic datasets of hyperspectral images confirm that SubXPCA classification performance is not only better than its competitors but also that the running time of SubXPCA is better in distributed processing than serial processing. As the size of the hyperspectral image dataset increased, SubXPCA showed a speed up factor of 5.7× and more in the spark cluster compared to the serial version.

关键词： Dimensionality reduction distributed processing Accuracy Satellites Partitioning algorithms Information and communication technology Sparks Hyperspectral imaging Principal component analysis Synthetic data

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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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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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