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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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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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On the Localization of Sound Sources with Moving Microphones 21

On the Localization of Sound Sources with Moving Microphones

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21. ITG/GMA-Fachtagung Sensoren und Messsysteme - 21st ITG/GMA conference on Sensors and Measuring Systems

作者： Rittenschober, Thomas Seven Bel GmbH Linz Austria

ISBN: (纸本)9783800758364

This contribution describes a novel method for visualizing sound sources using a rotating linear array of a few digital microphones. The rotating array scans the incident sound field on a circular area. While the reduced hardware complexity allows for the design of a lightweight, handheld sound imaging device, the algorithmic portion ofthe measurement system requires special attention. In fact, established methods of sound imaging like beamforming and nearfield holography cannot be applied to signals stemming from moving sensors. The proposed method of computing an acoustic image using the described measurement setup is based on compensating the moving microphone signals for Doppler distortions and evaluating the coherence ofthe resulting signals with a non-moving reference microphone for each point in the acoustic image. The method is evaluated in the context of leakage detection in automotive structures. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： parallel processing systems

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Contrastive Learning for Controllable Blind Video Restoration 33

Contrastive Learning for Controllable Blind Video Restoratio...

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33rd British Machine Vision conference Proceedings, BMVC 2022

作者： Meishvili, Givi Djelouah, Abdelaziz Hattori, Sally Schroers, Christopher Microsoft DisneyResearch|Studios Zurich Switzerland The Walt Disney Company Los Angles United States

A lot of progress has been made since the first neural network models were trained for specific image restoration tasks, such as super-resolution and denoising. Recently multi-degradation models have been proposed, allowing for user control of the restoration process needed for real-world applications. However, this aspect is most powerful if the initial restoration can be done as best as possible in a blind setting. In parallel to this line of work, other methods can target the blind setting where, for example, in the case of super-resolution, the blur kernel is estimated for conditioning the restoration part. In particular, discriminative learning has played a key role in pushing the state of the art. Still, the learned representation cannot be interpreted or manipulated and remains a black box that doesn't offer any possibility for user-guided correction. This work addresses those issues through a representation learning pipeline that helps separate content from degradation by reasoning on pairs of degraded patches. The degradation representation is used as conditioning for a video restoration model that can denoise and upscale to arbitrary resolutions and remove film scratches. Finally, the learned representation can be mutated to fine-tune the restoration results. We demonstrate state-of-the-art results compared to the most recent video super-resolution and denoising methods. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： image reconstruction

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Towards distributed Bitruss Decomposition on Bipartite Graphs 48th

Towards Distributed Bitruss Decomposition on Bipartite Graph...

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48th International conference on Very Large Databases (VLDB)

作者： Wang, Yue Xu, Ruiqi Jian, Xun Zhou, Alexander Chen, Lei Shenzhen Inst Comp Sci Shenzhen Peoples R China Natl Univ Singapore Singapore Singapore Hong Kong Univ Sci & Technol Hong Kong Peoples R China

Mining cohesive subgraphs on bipartite graphs is an important task. The k-bitruss is one of many popular cohesive subgraph models, which is the maximal subgraph where each edge is contained in at least k butterflies. The bitruss decomposition problem is to find all k-bitrusses for k >= 0. Dealing with large graphs is often beyond the capability of a single machine due to its limited memory and computational power, leading to a need for efficiently processing large graphs in a distributed environment. However, all current solutions are for a single machine and a centralized environment, where processors can access the graph or auxiliary indexes randomly and globally. It is difficult to directly deploy such algorithms on a shared-nothing model. In this paper, we propose distributed algorithms for bitruss decomposition. We first propose SC-HBD as the baseline, which uses H-function to define bitruss numbers and computes them iteratively to a fix point in parallel. We then introduce a subgraph-centric peeling method SC-PBD, which peels edges in batches over different butterfly complete subgraphs. We then introduce local indexes on each fragment, study the butterfly-aware edge partition problem including its hardness, and propose an effective partitioner. Finally we present the bitruss butterfly-complete subgraph concept, and divide and conquer DC-BD method with optimization strategies. Extensive experiments show the proposed methods solve graphs with 30 trillion butterflies in 2.5 hours, while existing parallel methods under shared-memory model fail to scale to such large graphs.

关键词： Iterative methods

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License Plate Detection methods Based on OpenCV 21

License Plate Detection Methods Based on OpenCV

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21st ACIS International Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Winter)

作者： Xu, Lin Shang, Wenqian Lin, Weiguo Huang, Wei Commun Univ China Sch Comp Sci & Cybersecur Beijing Peoples R China

ISBN: (纸本)9781665418430

With the popularization of automobile and the progress of computer vision detection technology, intelligent license plate detection technology has gradually become an important part of intelligent traffic *** plate detection is used to segment vehicle image and obtain license plate area for follow-up recognition system to screen. It is widely used in intelligent traffic management, vehicle video monitoring and other *** this paper, two license plate detection methods are studied, one is based on Sobel edge detection and the other is based on morphological gradient *** on OpenCV and visual studio 2012 under Windows system, two methods of license plate detection are implemented, and the two algorithms are compared in detail from the aspects of license plate detection *** methods have high efficiency and good interactivity, which provide a reference for later license plate recognition.

关键词： Intelligent Transportation System License Plate Detection Edge Detection Morphological Gradient Detection

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Comprehensive Analysis and Benchmarking of De-raining Techniques Through a Rain Model Approach

Comprehensive Analysis and Benchmarking of De-raining Techni...

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conference on Multimodal image Exploitation and Learning

作者： Badrieva, Shokhina Bernabel, Silvano Agaian, Sos GC CUNY Coll Staten Isl CSI Dept Comp Sci New York NY 10314 USA

ISBN: (纸本)9781510650770;9781510650763

Adverse weather conditions, such as rain, impact the visual quality of images and significantly impact the performance of vision systems for drone-based video surveillance and self-driving car applications. It is essential to develop algorithms that can automatically remove these artifacts and not degrade the rest of the image. Several methods have been proposed in literature and practice to address this problem. They mainly focus on specific rain models, such as droplets, streaks, mist, or a combination of these. Real-life rain images are largely randomized with diverse rain sizes, types, densities, and directions. Furthermore, rain impacts various image parts differently and is often randomly distributed. Most existing de-raining algorithms can't remove drops, streaks, and mist from images simultaneously. This paper addresses this issue by reviewing existing algorithms and datasets through a rain model lens. We present surveys and quantitative benchmarking of state-of-the-art intelligence algorithms based on the rain types they aim to remove. While other review papers exist on single image de-raining, our work looks at and outlines the different algorithms and datasets available for each specific rain model. Finally, the paper makes the following contributions: Select the most recent state of the art algorithms and show their performance for each rain type on our combination dataset called the Combination Rain Model Dataset Offers insights on the issues that still exist in the developing field of image de-raining and future steps in the field

关键词： De-rain rain removal deep learning algorithms digital image processing de-raining image applications rain model rain streak raindrop rain mist

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CAPLC: Cellular Automata-Powered Lightweight Cipher-Unveiling Secure Communication in Resource-Constrained Environments Using Cellular Automata and Henon Map

CAPLC: Cellular Automata-Powered Lightweight Cipher-Unveilin...

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Next Generation Communication & Information processing (INCIP), International conference on

作者： Biswarup Yogi Satyabrata Roy Umashankar Rawat Shweta Redkar Utsav Upadhyay Dept. of Computational Sciences Brainware University Kolkata West Bengal India Dept. of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Data Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Computer Science & Engineering Sir Padampat Singhania University Udaipur Rajasthan India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

In today's interconnected world, ensuring secure communication in resource-constrained contexts is still challenging. This study offers CAPLC, a novel technique for lightweight encryption that uses Cellular Automata (CA) and Henon maps. Its goal is to improve security in such situations. The method uses 1D Cellular Automata for initial data transformation and incorporates the Henon map to improve encryption strength. We used conventional metrics to evaluate CAPLC's effectiveness on image encryption, and the findings were promising: NPCR of 99.6154%, UACI of 33.88235, and PSNR of 9.1924. These numbers show the method's capacity to dramatically change image while maintaining quality and anonymity. The implications of CAPLC go beyond image encryption, providing a scalable solution for secure communication in circumstances with limited computational resources. Future studies ought to explore CAPLC's real-time application and performance across various kinds of datasets, with a focus on the potential of merging CA with Henon maps to create efficient encryption methods for improving secure communications technology.

关键词： Measurement Merging Automata Medical services Information processing parallel processing Real-time systems Encryption Resilience Principal component analysis

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LMPDNET: TOF-PET List-Mode image Reconstruction Using Model-Based Deep Learning Method

LMPDNET: TOF-PET List-Mode Image Reconstruction Using Model-...

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IEEE International conference on image processing

作者： Chenxu Li Jingwan Fang Rui Hu Jianan Cui Huafeng Liu State Key Lab of Modern Optical Instrumentation Zhejiang University Hangzhou China

The integration of Time-of-Flight (TOF) information in the reconstruction process of Positron Emission Tomography (PET) improves image qualities. However, implementing the cutting-edge model-based deep learning methods for TOF-PET reconstruction is challenging due to the substantial memory requirements. In this study, we presented a novel model-based deep learning approach, LMPDNet, for TOF-PET reconstruction from list-mode data. We addressed the issue of real-time parallel computation of the projection matrix for list-mode data, and proposed an iterative model-based module that utilized a dedicated network model for list-mode data. Our experimental results indicated that the proposed LMPDNet outperformed traditional iteration-based TOF-PET list-mode reconstruction algorithms. Additionally, we compared the spatial and temporal consumption of list-mode data and sinogram data in model-based deep learning methods, demonstrating the superiority of list-mode data in model-based TOF-PET reconstruction.

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Dynamic Checkpointing for Heterogeneous IoT Devices Through Self-Referencing

Dynamic Checkpointing for Heterogeneous IoT Devices Through ...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Nan Wang Ziyi Wang Lijun Lu Zhiyuan Ma Qun Chao School of Information Science and Engineering East China University of Science and Technology Shanghai China Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Failure recovery is one of the most essential problems in Internet of Things (IoT) systems, and the conventional snapshot method is an effective way to solve this problem. However, snapshot methods lack specialized designs for heterogeneous IoT devices, and when implemented in edge devices, serious system interruptions occur and performance is impacted. To address these problems, a dynamic checkpointing strategy is proposed for IoT systems that consist of heterogeneous devices. Firstly, an anomaly detection network for snapshots (i.e., ADSnet) that combines long short-term memory networks with multilayer convolutional networks is used to learn the multidimensional features of system resource usage. Secondly, ADSnet is tuned during deployment to learn the behaviors of target devices, so that ADSnet can report the anomalies of target devices in the near future. Finally, a dynamic checkpointing strategy is proposed to dynamically create snapshots on the basis of the anomaly detection results. The experimental results show that the proposed ADSnet achieves 97.73% accuracy in detecting anomalies in the target device; furthermore, our proposed dynamic checkpointing strategy reduces 25.4% snapshots than that created by the recently proposed ResCheck.

关键词： Checkpointing Performance evaluation Accuracy Design methodology image edge detection Nonhomogeneous media Feature extraction Internet of Things Anomaly detection Long short term memory

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Quantum-Inspired Data processing for Big Data Analytics

Quantum-Inspired Data Processing for Big Data Analytics

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Advancement in Electronics & Communication Engineering (AECE), International conference on

作者： Sunil Raj Thota Saransh Arora Sandeep Gupta Technology Engineering Andhra University Visakhapatnam AP India Jaypee Institute of Information Technology Noida U.P. India Techieshubhdeep IT Solutions Pvt. Ltd Gwalior M.P. India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Technological advancements in the 21st century have led to the rise of "big data," characterized by datasets so vast and complex that traditional database systems struggle to manage them. This term denotes datasets that are too vast and complex to be processed by traditional database systems. As big data continues to evolve, it introduces significant challenges related to data processing, storage, and analysis. In response, quantum-inspired algorithms have emerged as a promising solution to these challenges. Quantum-inspired algorithms, which draw on principles from quantum computing, present a promising solution to the challenges of big data processing, storage, and analysis. Techniques such as Quantum Annealing, Quantum Circuits and Gates, and Quantum parallelism leverage quantum principles like superposition and entanglement to enhance data processing efficiency and speed. These methods offer significant improvements over traditional tools like Hadoop, MapReduce, RapidMiner, and R Programming, which are used for distributed storage, processing, and analytics of large datasets. By integrating quantum-inspired algorithms with existing data processing techniques, this study aims to address the limitations of classical methods and advance the field of big data analytics.

关键词： Data analysis Annealing Quantum entanglement Big Data Programming parallel processing Logic gates Quantum annealing Database systems Quantum circuit

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