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

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Pattern-Invariant Unrolling for Robust Demosaicking

Pattern-Invariant Unrolling for Robust Demosaicking

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European Signal processing conference (EUSIPCO)

作者： Matthieu Muller Daniele Picone Mauro Dalla Mura Magnus O. Ulfarsson GIPSA-Lab Univ. Grenoble Alpes CNRS Grenoble INP Grenoble France Faculty of Electrical and Computer Engineering University of Iceland Iceland

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

To acquire color images, most commercial cameras rely on color filter arrays (CFAs), which are a pattern of color filters overlaid over the sensor's focal plane. Demosaicking describes the processing techniques to reconstruct a full color image for all pixels on the focal plane array. Most demosaicking methods are tailored for a specific CFA, and tend to work poorly for others. In this work we present an algorithm for demosaicking a wide variety of CFAs. The proposed method allows to blend the knowledge of the CFA with information coming from data, employing a novel transformation and pattern-invariant loss function. The method is based on the unrolling of an algorithm based on a neural network learned on available examples. Preliminary experiments over RGB and RGBW CFAs show that the method performs well over a range of CFAs and is competitive for CFAs for which competing methods were tailored to work well on.

关键词： image color analysis Neural networks Signal processing algorithms Europe Color Filtering algorithms parallel processing Data models image reconstruction Sensor arrays

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Method for Generating Vector image Features for Contextual Search Tasks

Method for Generating Vector Image Features for Contextual S...

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International Russian Automation conference (RusAutoCon)

作者： Valentina Tarasova Mikhail Nikiforov Electronic computers department Ryazan state radio engineering university RSREU Ryazan Russian Federation

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

This article discusses a method for generating a vector of image features for the task of contextual search. The vector consists of numerical values obtained from texture and color features, key points, values obtained from a convolutional neural network, and features of the object in the image. The criterion for image similarity is also described. As part of this work, a convolutional neural network model was developed to highlight significant visual characteristics, and the dependence of color space and visual search accuracy was analyzed. The conducted studies showed the effectiveness of the proposed method for generating an image feature vector. However, this method is inferior in terms of vector formation time, since it consists of a combination of several methods. This indicator can be improved through the use of parallel computing technologies.

关键词： Measurement Training Visualization image color analysis Neural networks parallel processing Vectors Numerical models Convolutional neural networks Software tools

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Arabic sign Language Recognition: Towards a Dual Way Communication System Between Deaf and Non-Deaf People 22

Arabic sign Language Recognition: Towards a Dual Way Communi...

引用

22nd IEEE/ACIS International conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Fall)

作者： Ben Hamouda, Souha Gabsi, Wafa Univ Sfax ReDCAD Lab Natl Sch Engineers Sfax BP 1173 Sfax 3038 Tunisia Fac Sci Gabes City Riadh Zerig 6029 Gabes Tunisia

ISBN: (纸本)9781665404037

One key perspective when communicating with deaf people is sign language recognition. Many experts agree upon the fact that using a system communication is a key for bridging the gap between deaf and non deaf people since ordinary people can not exchange using the sign language. As a result, researchers, deaf people, parents and deaf-mute community are striving to have a bidirectional communication system based on translation with lower costs. The importance of the research is related to its goal of helping these categories of unvoiced people communicate with others and enhance their contributions to growth and capacity building and vice versa. This paper gives an overview of the most used techniques and technologies (gloves, android application, image processing, ...) in order to translate sign language to written or spoken language. Furthermore, this paper provides a critical and comparative analysis of the studied approaches and stands out major challenges to overcome their limits. Finally, we propose in this paper a dual way communication system ensuring arabic sign language translation into spoken language based on image processing and deep learning.

关键词： Arabic sign language Gesture recognition image processing Machine learning mobile application dual communication system

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2nd International conference on Advances in Computing Research, ACR 2024

2nd International Conference on Advances in Computing Resear...

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2nd International conference on Advances in Computing Research, ACR 2024

ISBN: (纸本)9783031569494

The proceedings contain 48 papers. The special focus in this conference is on Advances in Computing Research. The topics include: Public Policy Decision Making: Confirmatory Factor Analysis;An Analytical Study of Traffic Accidents in Connecticut, USA Using Python;improving the Efficiency of Multimodal Approach for Chest X-Ray;wrist Crack Classification Using Deep Learning and X-Ray Imaging;improving Weeds Detection in Pastures Using Illumination Invariance Techniques;credit Card Batch processing in Banking System;irregular Frame Rate Synchronization of Multi-camera Videos for Data-Driven Animal Behavior Detection;increasing the Accuracy of a Deep Learning Model for Traffic Accident Severity Prediction by Adding a Temporal Category;2ARTs: A Platform for Exercise Prescriptions in Cardiac Recovery Patients;understanding Consumers Attitudes Towards Sustainability;Enriching Ontology with Named Entity Recognition (NER) Integration;data Transfer methods and Strategies: Unified Replication Model Using Trees;optimized Vehicle Repair Cost by Means of Smart Repair Distribution Model;classification of Eye Disorders Using Deep Learning and Machine Learning Models;Effects of parallel and distributed Learning on CNN Performance for Lung Disease Classification;a Federated Learning Anomaly Detection Approach for IoT Environments;Taxonomy of AR to Visualize Laparoscopy During Abdominal Surgery;open Platform Infrastructure for Industrial Control Systems Security;Towards Hybrid NIDS: Combining Rule-Based SIEM with AI-Based Intrusion Detectors;security Challenges and Solutions in Smart Cities;cloudSec: An Extensible Automated Reasoning Framework for Cloud Security Policies;optimizing Energy Consumption for IoT Adaptive Security: A Mobility-Based Solution;cyber Edge: Mitigating Cyber-Attacks in Edge Computing Using Intrusion Detection System;A New Security Mechanism for IoT Devices: Electroencephalogram (EEG) Signals;an Assessment of the Cyber Security Challenges and Issues Associated wi

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A Multimodal Fusion Framework for Fake News Detection via Multi-Attention Mechanism

A Multimodal Fusion Framework for Fake News Detection via Mu...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Yongxin Yu Yanqiang Li Ke Ji Zhenxiang Chen Kun Ma Xiaofan Zhao School of Information Science and Engineering University of Jinan Jinan China School of Information and Cyber Security People’s Public Security University of China Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Social media platforms have emerged as the primary channels for the general public to access and share information. However, the rapid dissemination of news has led to the emergence of a significant amount of unverified content, posing a serious threat to media credibility and network security. The current solutions primarily focus on detecting fake news by extracting and fusing features from images and text, but they have not fully utilized the relevance within and between modalities, resulting in suboptimal fusion effects. In this paper, we propose a multimodal fusion framework via multi-attention mechanism (MFMA), which considers not only the semantic and relevance features of images, textual content, and Optical Character Recognition (OCR) text from the news, but also the multimodal relevance features between them. First, we extract text within images (OCR text) to address the issue of inadequate utilization of the visual modality in traditional methods. Second, we select more robust feature extractors to capture the independent characteristics of each modality and use a pre-trained FcaNet model to extract more comprehensive image quality features. Additionally, two relevance extraction modules are designed to achieve feature fusion within and between modalities. Finally, the combined features are fed into a classifier to determine the authenticity of the news. The experimental results and analysis indicate that the model we proposed effectively enhances the performance of fake news detection.

关键词： image quality Visualization distributed processing Social networking (online) Optical character recognition Semantics Network security Feature extraction Data mining Fake news

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OADA: An Online Data Augmentation Method for Raw Histopathology images 28th

OADA: An Online Data Augmentation Method for Raw Histopathol...

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28th International conference on Neural Information processing

作者： Wu, Zhiyue Wang, Yijie Mi, Haibo Xi, Hongzuo Zhang, Wei Feng, Lanlan Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Airforce Mil Med Univ Tangdu Hosp Dept Pathol Xian Peoples R China

ISBN: (纸本)9783030923099;9783030923105

Deep learning-based automatic medical diagnosis is intensively studied in recent years. Abundant clinical raw records can be utilized, but we demonstrate that mixed and unknown magnification scales and staining conditions of raw histopathology images greatly hinder many successful deep models in this task. To address this problem, this paper proposes an Online Adaptive Data Augmentation method (OADA). In each training epoch, OADA adaptively selects base images and determines the personalized augmentation size of each image based on the current training status. The chosen images are augmented to update the training set. Extensive experiments show that OADA-empowered deep models obtain significant improvement compared to their bare versions, and OADA outperforms a suite of data augmentation baselines and state-of-the-art competitors.

关键词： Histopathology image Online data augmentation Deep learning

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Experience Report on Teaching parallel and distributed Programming Through Storytelling

Experience Report on Teaching Parallel and Distributed Progr...

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Software Analysis, Evolution and Reengineering - Companion (SANER-C), IEEE International conference on

作者： Bogdan Iudean Computer Science Department Babes-Bolyai University Cluj-Napoca Romania

ISBN: (数字)9798350351576

ISBN: (纸本)9798350351583

Multi-core processors, cloud computing, and distributed applications now define the landscape of modern software development and research. Efficiently teaching multithreading concepts is vital to equip students for real-world industry challenges. Navigating the complexities of multithreading proves challenging for students due to the high level of abstraction and difficulty associated with these concepts. The complexity of these technological fields raises the necessity for a comprehensive educational approach that facilitates students to understand the multithreading functionalities and usages with ease. This experience paper details our teaching approach, utilizing narrative-driven methods in parallel and distributed programming laboratories. We emphasize the relevance, novelty, and benefits of integrating interactive storytelling into the tangled learning process of parallel and distributed programming concepts. Concurrency, parallelization, and workload distribution are explained through live narrations, accompanied by animated presentations following a procedural storyline that spans laboratories. Complex ideas are transformed into meaningful character interactions, like stone-age people training octopuses to make ice-cream concurrently. This unique approach aims not only to enhance comprehension but also to make learning a vibrant and memorable experience for students, bridging the gap between theory and practical application. Our report extracts valuable lessons from student activities, emphasizing the motivational impact of engaging narratives. Students reflect on the relevance of concepts, shifting focus from implementation to understanding. Beyond just facilitating and easier access to knowledge, our approach aims to raise a deeper appreciation for the intricate world of parallel and distributed programming domain by encouraging students to see beyond the technical depth.

关键词： Training Technological innovation Multithreading Navigation Multicore processing Laboratories Software

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Scalable SubXPCA with Extended Morphological Profiles Applied to Hyperspectral image Classification on a distributed Platform

Scalable SubXPCA with Extended Morphological Profiles Applie...

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IEEE International conference on Big Data

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

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The resolution of large volumes of high-dimensional hyperspectral images have increased significantly due to the ongoing advancements in satellite remote sensing technology. One of the most well-known, widely used, and open-source distributed processing framework is Apache Spark, which is proven to quickly handle enormous volumes of these remotely sensed hyperspectral images. Therefore, there is a need for dimensionality reduction algorithms to process these high dimensional images and achieve accurate application performance. This paper suggests a distributed approach for spectral and spatial dimensionality reduction of hyperspectral images. We implemented feature partitioning principal component analysis based on the SubXPCA method for spectral dimensionality reduction and Extended Morphological Profiles for spatial dimensionality reduction in a Spark cluster distributed environment. We compared the proposed method against other feature and non-feature partitioning dimensionality reduction methods with Extended Morphological Profiles. Our experiments on different real and synthetic hyperspectral image datasets confirm that the proposed spectral-spatial reduction method classification performance is better than the spectral reduction methods and their competitors. The proposed method’s running time decreases with an increase in the number of cores in the cluster. The proposed methodology showed a speedup factor of 7.12×, 13.17× and more in a standalone Spark cluster and a Google cloud Dataproc cluster respectively, with an increase in the number of cores.

关键词： Dimensionality reduction Satellites Runtime image resolution Partitioning algorithms Internet Sparks Hyperspectral imaging Principal component analysis image classification

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Im2win: An Efficient Convolution Paradigm on GPU 29th

Im2win: An Efficient Convolution Paradigm on GPU

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29th International conference on parallel and distributed Computing (Euro-Par)

作者： Lu, Shuai Chu, Jun Guo, Luanzheng Liu, Xu T. Nanchang Hangkong Univ Nanchang Jiangxi Peoples R China Univ Calif Merced Merced CA USA Univ Washington Seattle WA USA

ISBN: (纸本)9783031396977;9783031396984

Convolution is the most time-consuming operation in deep neural network operations, so its performance is critical to the overall performance of the neural network. The commonly used methods for convolution on GPU include the general matrix multiplication (GEMM)-based convolution and the direct convolution. GEMM-based convolution relies on the im2col algorithm, which results in a large memory footprint and reduced performance. Direct convolution does not have the large memory footprint problem, but the performance is not on par with GEMM-based approach because of the discontinuous memory access. This paper proposes a window-order-based convolution paradigm on GPU, called im2win, which not only reduces memory footprint but also offers continuous memory accesses, resulting in improved performance. Furthermore, we apply a range of optimization techniques on the convolution CUDA kernel, including shared memory, tiling, micro-kernel, double buffer, and prefetching. We compare our implementation with the direct convolution, and PyTorch's GEMM-based convolution with cuBLAS and six cuDNN-based convolution implementations, with twelve state-of-the-art DNN benchmarks. The experimental results show that our implementation 1) uses less memory footprint by 23.1% and achieves 3.5x TFLOPS compared with cuBLAS, 2) uses less memory footprint by 32.8% and achieves up to 1.8x TFLOPS compared with the best performant convolutions in cuDNN, and 3) achieves up to 155x TFLOPS compared with the direct convolution. We further perform an ablation study on the applied optimization techniques and find that the micro-kernel has the greatest positive impact on performance.

关键词： Convolution CUDA im2win im2col parallel computing CNN

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image Inpainting using Automatic Structure Propagation with Auxiliary Line Construction

Image Inpainting using Automatic Structure Propagation with ...

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Summer Virtual conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Summer), ACIS International

作者： Yuto Urano Irawati Nurmala Sari Weiwei Du Department of Information Science Kyoto Institute of Technology Kyoto Japan Information and Human Science Kyoto Institute of Technology Kyoto Japan

ISBN: (纸本)9798350396386

Existing image inpainting methods used traditional and deep learning methods to restore a large missing region in the damaged image. This often leads to color discrepancy and blurriness. Pre-processing of prior line detection by user assistance is usually employed to reduce the blurry of center region by segmenting the large region into more minor. However, it operates manually, which is time-consuming. This paper introduces a technique to generate two-line types: penetrator and interactor in constructing auxiliary lines as guidance. These lines assist structure propagation established automatically, while the remaining small regions are filled by texture propagation. Experiments on large regular masks demonstrate that our proposed approach generates higher-quality results than other methods.

关键词： Deep learning image segmentation image color analysis image restoration Artificial intelligence Software engineering

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