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distributed algorithms for scalable proximity operator computation and application to video denoising

DIGITAL SIGNAL processing 2022年第0期128卷

作者： Abboud, Feriel Stamm, Marin Chouzenoux, Emilie Pesquet, Jean-Christophe Talbot, Hugues WITBE Collines Arche Immeuble Opera F-92800 Puteaux La Defense France Inst Polytech Paris ENSTA Paris Palaiseau France Univ Paris Saclay Ctr Visual Comp Cent Supelec Inria Gif Sur Yvette France

Optimization problems arising in signal and image processing involve an increasingly large number of variables. In addition to the curse of dimensionality, another difficulty to overcome is that the cost function usually reads as the sum of several loss or regularization terms, which are non-necessarily smooth and possibly composed with large-size linear operators. Proximal splitting approaches are fundamental tools to address such problems, with demonstrated efficiency in many applicative fields. In this paper, we present a new distributed algorithm for computing the proximity operator of a sum of non-necessarily smooth convex functions composed with arbitrary linear operators. Our algorithm relies on a primal-dual splitting strategy, and benefits from established convergence guaranties. Each involved function is associated with a node of a hypergraph, with the ability to communicate with neighboring nodes sharing the same hyperedge. Thanks to this structure, our method can be efficiently implemented on modern parallel computing architectures, distributing the computations on multiple nodes or machines, with controlled requirements for synchronization steps. Good numerical performance and scalability properties are demonstrated on a problem of video sequence denoising. Our code implemented in Julia is made available at https://github .com /MarinENSTA /distributed_julia_denoising. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Convex optimization Proximal methods distributed algorithm parallel programming Video restoration

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A Strategy of parallel SLIC Superpixels for Handling Large-Scale images over Apache Spark

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REMOTE SENSING 2022年第7期14卷 1568-1568页

作者： Wang, Ning Chen, Fang Yu, Bo Wang, Lei Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci 9 Dengzhuang South Rd Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Hainan Key Lab Earth Observat Sanya 572029 Peoples R China

Superpixel segmentation algorithms are widely used in the image processing field. The size of the large-scale images usually exceeds the memory of a single machine given that the size of image data has increased rapidly in recent years. This leads to big challenges for implementing sequential superpixel segmentation methods, although these algorithms have good scalability. Additionally, segmentation of large-scale images over a distributed cluster is a feasible solution. Nevertheless, it is challenging to transplant sequential superpixel algorithms directly to a distributed environment, as usually there are incomplete object problems in the border area of image tiles. To overcome the incomplete object problems, one approach is to build a distributed strategy based on a sequential SLIC superpixel segmentation algorithm over a distributed cluster organized by Apache Spark. In our research, the decomposed image tiles were divided into two categories-even tiles and odd tiles. The even tiles were first segmented by the SLIC algorithm, then the cluster centers and buffer sizes of even tiles were extracted and switched to odd tiles. During the shuffle stage, the odd tiles acquired pixels from adjacent even tiles according to the buffer sizes, and then the buffered odd tiles were segmented by the SLIC algorithm with the help of the shared cluster centers. The superpixels with shared cluster centers were generated in even tiles and remained in order to enlarge the odd tiles rather than redundant computing of specific areas to modify incomplete superpixels well. Specifically, this strategy employs the shared variables to transmit intermediate results and the shuffle operations were carried out among approximately half of the entire image tiles, which reduces the communications further. The distributed strategy was evaluated in terms of the accuracy and execution efficiency, which revealed that the proposed strategy could not only get better F-measure values but is also implem

关键词： superpixel algorithms distributed computation image processing Apache Spark digital disaster reduction

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Edge-enhanced GAN with Vanishing Points for image Inpainting

Edge-enhanced GAN with Vanishing Points for Image Inpainting

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

作者： Kei Masaoka 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

Reconstructing the damaged images with perspective views has an extensive range in the field of image inpainting. However, most existing methods generated inadequately realistic restored images. Accomplishing this problem, we propose an edge-enhanced image generation model considering viewpoints. Our method applies edge map information to guide image generation based on the perspective views of an image using vanishing points detection. Texture synthesis will be presented as post-processing to complete the remaining missing regions. Experiment shows that our approach can generate perspective images with convincing details, such as indoor and outdoor facades.

关键词： image synthesis image edge detection Generative adversarial networks image restoration Artificial intelligence image reconstruction Software engineering

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A new scalable distributed k-means algorithm based on Cloud micro-services for High-performance computing

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parallel COMPUTING 2021年 101卷 102736-102736页

作者： Benchara, Fatema Zahra Youssfi, Mohamed Hassan II Univ Casablanca Lab SSDIA ENSET Mohammedia Mohammadia Morocco

The paper aims to propose a distributed clustering method for High performance computing (HPC) models and, its application for medical image processing. The communication cost is one of the great challenges, which minimizes the scalability of parallel and distributed computing models. Indeed, it reduces significantly the performance of HPC systems where these models are assigned to be implemented. In this paper, we present a new distributed k-means method which integrates virtual parallel distributed computing model with a low communication cost mechanism. The k-means method is performed as a distributed service within a cooperative micro-services team which uses asynchronous communication mechanism based on AMQP protocol. We design and implement a parallel and distributed HPC application for MRI image segmentation assigned to be deployed on cloud. Experimental results show that the proposed method (DSCM) and its assigned model reach high degree of scalability. We expect this clustering approach to provide scalable HPC applications for big data clustering.

关键词： Data Clustering parallel and distributed system Micro-services architecture distributed k-means algorithm MRI image segmentation

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distributed Acoustic Sensing for Vehicle Speed and Traffic Flow Estimation

Distributed Acoustic Sensing for Vehicle Speed and Traffic F...

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IEEE Intelligent Transportation Systems Conference (ITSC)

作者： Wiesmeyr, Christoph Coronel, Carmina Litzenberger, Martin Doeller, Herbert Josef Schweiger, Hans-Bernhard Calbris, Gaetan AIT Austrian Inst Technol GmbH Vienna Austria Dr Doller Vermessung ZT GmbH Vienna Austria Febus Opt Pau France

ISBN: (纸本)9781728191423

For traffic monitoring systems it is desirable to have a low-cost, low-maintenance, and reliable system to facilitate continuous and safe traffic flow. In line with this, we introduce distributed acoustic sensing (DAS) for traffic monitoring applications. In DAS systems, series of light pulses are transmitted along a fiber-optic cable and the back-scattered light, which is affected by the mechanical strain of the fiber-optic cables due to ground vibrations, is measured and analyzed. With fiber-optic cables installed parallel to a highway where ground vibrations are induced by passing vehicles, traffic information such as vehicle flow and average speeds can be estimated from the DAS signals. In this paper, we present an algorithm based on image processing methods that estimates traffic information from image representations generated from 1-minute segments of DAS signals. We tested the results of the algorithm on DAS data recording on a real highway against reference traffic data measured by road-side sensors.

关键词： Vibrations Optical fiber sensors Roads Optical fiber cables Estimation Mechanical cables image representation

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Development of methods for parallel processing of series of images obtained by a machine vision system in various electromagnetic ranges 2

Development of methods for parallel processing of series of ...

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Conference on Optical Instrument Science, Technology, and Applications ii Held as Part of SPIE Optical System Design Conference

作者： Semenishchev, Evgenii Voronin, Viacheslav Alepko, Andrey Zelensky, Aleksandr Agaian, Sos Moscow State Tech Univ STANKIN Moscow Russia CUNY Dept Comp Sci New York NY 10314 USA

ISBN: (数字)9781510645974

ISBN: (纸本)9781510645974;9781510645967

Machine vision systems used in modern industrial complexes, based on the analysis of multi and hyperspectral imaging. The transition to implementing the "Industry 4.0" program is not possible when using one type of data. The first control system used only the visible range image. They made it possible to analyze the trajectories of movement of objects, control product quality, carry out security functions (control of perimeter crossing), etc. The development of new industrial robotic cells and processing complexes using cognitive functions implying the receipt, analysis, and processing of heterogeneous data. The construction of a unified information field, which allows performing multidimensional operations with data, allows increasing the speed of decision-making and the implementation of automated robot-human systems at the level of an assistant working in a unified workspace. The use of machine vision systems analyzing information received in: visible (shape, the trajectory of movement, position of objects, etc.);near-infrared range (data is similar to visible, allows operation in dusty, foggy, low light conditions);far-infrared range - thermal (plotting temperature gradients, identifying areas of overheating);ultraviolet range (analysis of ionization sources, corona discharges, static charges, tags);X-ray and microwave ranges (analysis of the surface and internal structure of objects, allow the identification of defects);range and 3D sensors (construction of volumetric figures, analysis of the relative position of objects and their interaction), etc. Data analysis is often performed not by a single camera but by a group of sensors located not in a single housing. Primary data integration reduces the number of information channels while maintaining the functionality and accuracy of the analysis. The article discusses creating fusion images obtained by industrial sensors into a combined image containing joint data. Combining multi and hyperspectral imaging makes i

关键词： preprocessing IR image local features denoising combining image fusion image

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Intelligent Framework for Early Prediction of Type – ii Diabetes by Accelerating Palm Print images using Graph Data Science

Intelligent Framework for Early Prediction of Type – II Dia...

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IEEE distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Akhila Thejaswi R Bellipady Shamantha Rai Permanki Guthu Rithesh Pakkala Neha G Karthik J Kshiteesh Desai Sahyadri College of Engineering & Management Mangaluru Visvesvaraya Technological University Belagavi Karnataka India Department of Information Science & Engineering Sahyadri College of Engineering & Management Mangaluru Visvesvaraya Technological University Belagavi Karnataka India

Worldwide, Type ii Diabetes Mellitus (T2DM) is a major condition whose incidence is rising. It is a public health issue as well as the silent killer of humanity. Its diverse aetiology, which includes genetic predisposition and environmental influences, may offer a distinctive trait in the population that is useful for early diagnosis. As a result, researchers are seeking novel approaches to its early diagnosis and treatment. Now, manual methods are used to measure and compare the axial triradius (ATD) angles of both palms using a Goniometer for the early prediction of T2DM. The creation of datasets containing individuals' left and right palm prints is the primary objective of this project, which aims to develop a model that would assist in the early prediction of T2DM. The model would have the option to track down the distinction in the ATD points in the two palms and foresee assuming that the singular will be determined to have T2DM in the future. A total of 390 instances were collected out of which 130 are diabetic and 260 were non-diabetic patients. It consists of various image pre-processing techniques and delta-point detection methods. The model would be able to predict the results if the user simply uploaded images of both palms. The image Thresholding and Segmentation Method was used to evaluate the best features and the crossing number method was used to automate the model, indicating that the model achieved an accuracy of 91.02% in predicting the ATD angle. Using biomarkers, this project can be implemented in the health sector for early detection of Type ii Diabetes Mellitus.

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SMT-D: New Strategies for Portfolio-Based SMT Solving

SMT-D: New Strategies for Portfolio-Based SMT Solving

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Formal methods in Computer Aided Design (FMCAD)

作者： Clark Barrett Pei-Wei Chen Byron Cook Bruno Dutertre Robert B. Jones Nham Le Andrew Reynolds Kunal Sheth Christopher Stephens Michael W. Whalen Amazon Web Services Seattle USA Stanford University Stanford USA University of California Berkeley USA University of Waterloo Warterloo Canada University of Iowa Iowa City USA University of Illinois Urbana-Champaign USA

ISBN: (数字)9783854480655

ISBN: (纸本)9798331524784

We introduce SMT-D, a tool for portfolio-based distributed SMT solving. We propose a general architecture consisting of two main components: (i) solvers extended with the capability of sharing and importing information on the fly while solving; and (ii) a central manager that orchestrates and monitors solvers while also deciding which information to share with which solvers. We introduce new information-sharing strategies based on the idea of maximizing the amount of useful diversity in the system. We show that on hard benchmarks from recent related work, SMT-D instantiated with the cvc5 SMT solver achieves significant speed-up over sequential performance, is competitive with existing portfolio approaches, and contributes a number of unique solutions.

关键词： Scalability Diversity reception Symbols Benchmark testing parallel processing Solids Logic Portfolios Tuning Monitoring

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3rd International Symposium on Signal and image processing, ISSIP 2020

3rd International Symposium on Signal and Image Processing, ...

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3rd International Symposium on Signal and image processing, ISSIP 2020

ISBN: (纸本)9789813369658

The proceedings contain 16 papers. The special focus in this conference is on Signal and image processing. The topics include: Deep Convolutional Neural Network-Based Diagnosis of Invasive Ductal Carcinoma;speaker Identification in Spoken Language Mismatch Condition: An Experimental Study;Ultrasound image Classification Using ACGAN with Small Training Dataset;preface;Chaotic Ions Motion Optimization (CIMO) for Biological Sequences Local Alignment: COVID-19 as a Case Study;assessment of Eyeball Movement and Head Movement Detection Based on Reading;using Hadoop Ecosystem and Python to Explore Climate Change;a Brief Review of Intelligent Rule Extraction Techniques;the Effect of Different Feature Selection methods for Classification of Melanoma;intelligent Hybrid Technique to Secure Bluetooth Communications;parallel Algorithm to find Integer k where a given Well-distributed Graph is k-Metric Dimensional;a Fog-Based Retrieval of Real-Time Data for Health Applications;differential Evolution-Based Shot Boundary Detection Algorithm for Content-Based Video Retrieval;qutrit-Based Genetic Algorithm for Hyperspectral image Thresholding.

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Kulla, a container-centric construction model for building infrastructure-agnostic distributed and parallel applications

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JOURNAL OF SYSTEMS AND SOFTWARE 2020年 168卷 110665-110665页

作者： Reyes-Anastacio, Hugo G. Gonzalez-Compean, J. L. Sosa-Sosa, Victor J. Carretero, Jesus Garcia-Blas, Javier CINVESTAV Unidad Tamaulipas Km 5-5 Carr Soto la Marina Victoria 87130 Tamaulipas Mexico Univ Carlos III Madrid Comp Sci & Engn Dept Av Univ 30 Madrid 28911 Spain

This paper presents the design, development, and implementation of Kulla, a virtual container-centric construction model that mixes loosely coupled structures with a parallel programming model for building infrastructure-agnostic distributed and parallel applications. In Kulla, applications, dependencies and environment settings, are mapped with construction units called Kulla-Blocks. A parallel programming model enables developers to couple those interoperable structures for creating constructive structures named Kulla-Bricks. In these structures, continuous dataflow and parallel patterns can be created without modifying the code of applications. methods such as Divide&Containerize (data parallelism), Pipe&Blocks (streaming), and Manager/Block (task parallelism) were developed to create Kulla-Bricks. Recursive combinations of Kulla instances can be grouped in deployment structures called Kulla-Boxes, which are encapsulated into VCs to create infrastructure-agnostic parallel and/or distributed applications. Deployment strategies were created for Kulla-Boxes to improve the IT resource profitability. To show the feasibility and flexibility of this model, solutions combining real-world applications were implemented by using Kulla instances to compose parallel and/or distributed system deployed on different IT infrastructures. An experimental evaluation based on use cases solving satellite and medical image processing problems revealed the efficiency of Kulla model in comparison with some traditional state-of-the-art solutions. (C) 2020 Published by Elsevier Inc.

关键词： Infrastructure-agnostic applications Construction model Virtual containers parallel patterns Pipelines

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