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8th International Conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Chowdary, T. Anil Nalluri, Purnachand VIT-AP University School of Electronics Engineering Inavolu Andhra Pradesh 522237 India

ISBN: (纸本)9781665408165

Tools for Transform Coding in coding of video relied on DCT-II traditionally for mapping residuals of image/video signals. Residual mapping can be done to a domain where quantizing and encoding tools give better efficiency in coding than domain of spatial. Nonetheless, DCT-II cannot model available types of residual image/video signals effectively. DCT-II modeling all types of residuals is not possible because of non-stationary behaviour of contents of video. So, to improve performance in compression of 4 X 4 blocks, DST-vii was instigated into VVC. In this paper This Paper presents survey of different algorithms for selection of optimal transform for each block (4X4, 8X8, 16X16, 32X32 and 64X64) of residual image/video for VVC. © 2022 IEEE.

关键词： Video signal processing

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Artificial Intelligence in Natural Disaster: Data Crawling- and Text Mining-Based Literature Review 8

Artificial Intelligence in Natural Disaster: Data Crawling- ...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Ramadhan, Zhafran Yunita, Ariana Universitas Pertamina Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350342604

A literature review is an essential part of research. Beginning researchers who would like to conduct research in any field commonly review previous papers to identify trends and gaps in research. However, conducting a literature review is challenging, particularly for novice researchers. First, synthesising previous related works can be time-consuming. Second, accessing expensive databases such as Scopus and Web of Science might be inaccessible for novice researchers in developing countries. Thus, the purpose of this paper is to present a review performed by data crawling ArXiv and using latent Dirichlet allocation (LDA), a topic modelling algorithm. In this paper, we provide an overview of the topic models of natural disasters and artificial intelligence. Papers from 2017-2022 were crawled using Python with specific keywords, and only papers written in English were eligible for further analysis. Then, the papers were pre-processed by lowering the text, removing punctuation and stop words, tokenising, and lemmatising with POS tagging techniques. Pre-processed papers were then trained using the LDA algorithm. For evaluation, coherence metrics were used to determine the optimal number of topics. There were five topics resulting from the topic modelling algorithm. The clusters were qualitatively analysed and labelled as image classification, algorithms, social media analysis, natural language processing, and machine learning. Further findings are also discussed. © 2023 IEEE.

关键词： Natural language processing systems

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From Linear to Nonlinear Unfolded Condat-Vú Algorithm for Spectro-Polarimetric Hight-Constrast image Recovery

From Linear to Nonlinear Unfolded Condat-Vú Algorithm for S...

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European Signal processing Conference (EUSIPCO)

作者： E. Chappon N. Pustelnik J. Tachella L. Denneulin A. Ferrari M. Langlois Laboratoire de Physique ENSL CNRS UMR 5672 Lyon France Laboratoire de Recherche de l'EPITA EPITA Le Kremlin-Bicêtre France Universite Côte d'Azur Observatoire de la Côte d'Azur CNRS Lab. J.-L. Lagrange France Univ Lyon Univ. Lyon 1 ENS de Lyon CNRS Centre de Recherche Astrophysique de Lyon UMR5574 Saint-Genis-Laval France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Studying circumstellar environments is crucial for understanding exoplanets and stellar systems. Instruments like SPHERE can extract information about these environments by leveraging advanced image reconstruction methods, possibly based on deep learning. This work focuses on unfolded proximal neural networks based on Condat- vii iterations and proposes a new nonlinear formulation. To evaluate and compare the performance of the proposed reconstruction strategies, two datasets dedicated to circumstellar environments analysis in the context of high-contrast imagery have been created offering different level of complexity in the evaluation of the performance.

关键词： Deep learning Training image color analysis Neural networks Signal processing algorithms Signal processing image reconstruction Standards Synthetic data Finite difference methods

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A real-time person tracking system based on SiamMask network for intelligent video surveillance

A real-time person tracking system based on SiamMask network...

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作者： Ahmed, Imran Jeon, Gwanggil Centre for Excellence in Information Technology IMSciences Peshawar 1-A Sector E-5 Phase VII Hayatabad Peshawar Pakistan Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

Real-time video surveillance systems are widely deployed in various environments, including public areas, commercial buildings, and public infrastructures. Person detection is a key and crucial task in different video surveillance applications, such as person detection, segmentation, and tracking. Researchers presented different image processing and artificial intelligence-based approaches (including machine and deep learning) for person detection and tracking, but mainly comprised of frontal view camera perspective. A real-time person tracking and segmentation system is introduced in this work, using an overhead camera perspective. The system applied a deep learning-based algorithm, i.e., SiamMask, a simple, versatile, fast, and surpassing other real-time tracking algorithms. The algorithm also performs segmentation of the target person by combining a mask branch to the fully convolutional twin neural network for target or person tracking. First, the person video sequences are obtained from an overhead perspective, and then additional training is performed with the help of transfer learning. Finally, a comparison is performed with other tracking algorithms. The SiamMask algorithm delivers good results, with a tracking accuracy of 95%. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Monitoring

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Artificial Intelligence for the Diagnosis of Photovoltaic systems Using Thermography : Inteligencia Artificial para el Diagnóstico de Sistemas Fotovoltaicos mediante Termografías

Artificial Intelligence for the Diagnosis of Photovoltaic Sy...

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Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC), V Congreso Internacional en

作者： Pincay-Lozada Jorge Luis Jimenez-Castillo Gabino Mulcue-Nieto Luis Fernando Lopez-Sotelo Jesus Alfonso Muñoz-Rodriguez Francisco Departamento CyTI Universidad de San Buenaventura Universidad de Jaén Universidad Autónoma de Occidente Cali Colombia Departamento de Ingeniería Electrónica y Automática Universidad de Jaén Jaén España Departamento de Física y Matemática Universidad de Manizales Manizales Colombia Departamento de Automática y Electrónica Universidad Autónoma de Occidente Cali Colombia José Ingeniería Electrónica y Automática Universidad de Jaén Jaén Colombia

ISBN: (数字)9798350364538

ISBN: (纸本)9798350364545

This predoctoral research project is carried out in the framework of an international co-tutelage between the University of Jaén and the Universidad Autónoma de Occidente in Cali, Colombia with the participation of the CyTI Department of the Universidad de San Buenaventura. Its main objective is to systematize the processes of maintenance and diagnosis of modules in Photovoltaic systems (PVS) using computational tools based on Artificial Intelligence (AI). The project seeks to reduce operating costs, minimize human errors and detect possible failures early, in order to extend the operating hours of the PV systems and reduce the time spent on preventive maintenance. To achieve these objectives, deep learning algorithms are used in infrared (IR) image processing. These algorithms make it possible to evaluate the state of the photovoltaic modules by analyzing variations in surface temperatures, detecting anomalous situations and failures in each photovoltaic (PV) collector module. The implementation of these techniques will contribute to the development of effective methodologies that will significantly improve PV maintenance. This advance represents significant progress in the efficiency and sustainability of solar photovoltaic energy, with applications of great relevance in both the scientific and technological *** proyecto de investigación predoctoral se lleva a cabo en el marco de una cotutela internacional entre la Universidad de Jaén y la Universidad Autónoma de Occidente en Cali, Colombia con la participación del Departamento de CyTI de la Universidad de San Buenaventura. Su objetivo principal es sistematizar los procesos de mantenimiento y diagnóstico de módulos en Sistemas Fotovoltaicos (SFV) mediante el uso de herramientas computacionales basadas en Inteligencia Artificial (IA). El proyecto busca reducir costos operativos, minimizar errores humanos y detectar tempranamente posibles fallos, con el fin de prolongar las horas de funcionamiento de los SFV

关键词： Deep learning image processing Infrared imaging Software Maintenance Solar panels Artificial intelligence Sustainable development Surface treatment Preventive maintenance

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Multi-Cascade image Correction System for the Large Solar Vacuum Telescope 7

Multi-Cascade Image Correction System for the Large Solar Va...

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Conference on Adaptive Optics systems vii

作者： Lukin, V. P. Botygina, N. N. Antoshkin, L., V Borzilov, A. G. Emaleev, O. N. Konyaev, P. A. Kovadlo, P. G. Kolobov, D. Yu Shikhovtsev, A. Yu Chuprakov, S. A. Russian Acad Sci VE Zuev Inst Atmospher Opt Siberian Branch Tomsk 634055 Russia Russian Acad Sci Inst Solar Terr Phys Siberian Branch Irkutsk 664033 Russia

ISBN: (纸本)9781510636842

A multi-cascade adaptive optical system for imaging and image stabilization for the Large Solar Vacuum Telescope is described. This system was created in 2017 by specialists of the V.E. Zuev Institute of Atmospheric Optics SB RAS, Tomsk, with the technical support of the Institute of Solar-Terrestrial Physics SB RAS, Irkutsk. The system has been tested at the Large Solar Vacuum Telescope (Baikal Astrophysical Observatory) and demonstrated its efficiency. Along with the first cascade of adaptive image stabilization by a tip-tilt corrected mirror, this system employs the second imaging cascade based on correction with a flexible mirror controlled by a specialized wavefront sensor, as well as the third cascade for real-time post-detector processing of video camera frames. Reliable experimental data confirming the efficiency of the multi-cascade adaptive system for image formation and stabilization have been obtained. Three high-rate digital video cameras recording simultaneously digital images with rates from 300 to 980 frames per second were used to test the system. The mirror correcting wavefront tilts and operating in a closed optical feedback loop was controlled by the specially developed software including the fast correlation tracking algorithm. The post-detector digital imaging was performed with a special software for processing of video camera frames in real time with the use of modern high-speed parallel algorithms based on the Intel MKL and IPP libraries.

关键词： astronomy solar telescope astroclimate turbulence adaptive optics tilt corrector correction cascade

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Exoplanet Imaging Data Challenge: benchmarking the various image processing methods for exoplanet detection 7

Exoplanet Imaging Data Challenge: benchmarking the various i...

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Conference on Adaptive Optics systems vii

作者： Cantalloube, F. Gomez-Gonzalez, C. Absil, O. Cantero, C. Bacher, R. Bonse, J. M. Bottom, M. Dahlqvist, C. H. Desgrange, C. Flasseur, O. Fuhrmann, T. Henning, Th Jensen-Clem, R. Kenworthy, M. Mawet, D. Mesa, D. Meshkat, T. Mouillet, D. Mueller, A. Nasedkin, E. Pairet, B. Pierard, S. Ruffio, J. B. Samland, M. Stone, J. Van Droogenbroeck, M. Max Planck Inst Astron Konigstuhl 17 D-69117 Heidelberg Germany Barcelona Supercomp Ctr Barcelona Spain Univ Liege Space Sci Technol & Astrophys Res STAR Inst Allee Six Aout 19c B-4000 Liege Belgium Univ Liege Montefiore Inst B-4000 Liege Belgium Univ Grenoble Alpes IPAG CNRS F-38000 Grenoble France Swiss Fed Inst Technol Inst Particle Phys & Astrophys Wolfgang Pauli Str 27 CH-8093 Zurich Switzerland CALTECH Jet Prop Lab Pasadena CA 91125 USA Ecole Normale Super Lyon Dept Phys F-69364 Lyon France Univ Lyon Univ Lyon1 ENS Lyon CNRSCtr Rech Astrophys LyonUMR 5574 F-69230 St Genis Laval France Ostbayer TH Regensburg Germany Univ Calif Santa Cruz Astron Dept 1156 High St Santa Cruz CA 95064 USA Leiden Univ Leiden Observ POB 9513 NL-2300 RA Leiden Netherlands CALTECH Dept Astron 1200 E Calif Blvd Pasadena CA 91125 USA INAF Osservatorio Astron Padova Vicolo Osservatorio 5 I-35122 Padua Italy UCLouvain ELEN ICTEAM ISPGrp B-1348 Louvain La Neuve Belgium Stockholm Univ Dept Astron Stockholm Sweden Univ Arizona Steward Observ 933 N Cherry Ave Tucson AZ 85721 USA

ISBN: (纸本)9781510636842

The Exoplanet Imaging Data Challenge is a community-wide effort meant to offer a platform for a fair and common comparison of image processing methods designed for exoplanet direct detection. For this purpose, it gathers on a dedicated repository (Zenodo), data from several high-contrast ground-based instruments worldwide in which we injected synthetic planetary signals. The data challenge is hosted on the CodaLab competition platform, where participants can upload their results. The specifications of the data challenge are published on our website https://***/. The first phase, launched on the 1st of September 2019 and closed on the 1st of October 2020, consisted in detecting point sources in two types of common data-set in the field of high-contrast imaging: data taken in pupil-tracking mode at one wavelength subchallenge 1, also referred to as ADI) and multispectral data taken in pupil-tracking mode (subchallenge 2, also referred to as ADI+mSDI). In this paper, we describe the approach, organisational lessons-learnt and current limitations of the data challenge, as well as preliminary results of the participants' submissions for this first phase. In the future, we plan to provide permanent access to the standard library of data sets and metrics, in order to guide the validation and support the publications of innovative image processing algorithms dedicated to high-contrast imaging of planetary systems.

关键词： Exoplanet detection High-contrast imaging Adaptive Optics Coronagraphy Post-processing techniques Data challenge

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Long Term Safety Area Tracking (LT-SAT) with online failure detection and recovery for robotic minimally invasive surgery

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MEDICAL image ANALYSIS 2018年 45卷 13-23页

作者： Penza, Veronica Du, Xiaofei Stoyanov, Danail Forgione, Antonello Mattos, Leonardo S. De Momi, Elena Politecn Milan Dept Elect Informat & Bioengn Pzza L Da Vinci 32 I-20133 Milan Italy Ist Italiano Tecnol Dept Adv Robot Via Morego 30 I-16163 Genoa Italy UCL Ctr Med Image Comp Dept Comp Sci London England Osped Niguarda Ca Granda Pzza Osped Maggiore 3 I-20162 Milan Italy

Despite the benefits introduced by robotic systems in abdominal Minimally Invasive Surgery (MIS), major complications can still affect the outcome of the procedure, such as intra-operative bleeding. One of the causes is attributed to accidental damages to arteries or veins by the surgical tools, and some of the possible risk factors are related to the lack of sub-surface visibilty. Assistive tools guiding the surgical gestures to prevent these kind of injuries would represent a relevant step towards safer clinical procedures. However, it is still challenging to develop computer vision systems able to fulfill the main requirements: (i) long term robustness, (ii) adaptation to environment/object variation and (iii) real time processing. The purpose of this paper is to develop computer vision algorithms to robustly track soft tissue areas (Safety Area, SA), defined intra-operatively by the surgeon based on the real-time endoscopic images, or registered from a pre-operative surgical plan. We propose a framework to combine an optical flow algorithm with a tracking-by-detection approach in order to be robust against failures caused by: (i) partial occlusion, (ii) total occlusion, (iii) SA out of the field of view, (iv) deformation, (v) illumination changes, (vi) abrupt camera motion, (vii), blur and (viii) smoke. A Bayesian inference-based approach is used to detect the failure of the tracker, based on online context information. A Model Update Strategy (MUpS) is also proposed to improve the SA re-detection after failures, taking into account the changes of appearance of the SA model due to contact with instruments or image noise. The performance of the algorithm was assessed on two datasets, representing ex-vivo organs and in-vivo surgical scenarios. Results show that the proposed framework, enhanced with MUpS, is capable of maintain high tracking performance for extended periods of time (similar or equal to 4 min - containing the aforementioned events) with high precisi

关键词： Long-term tissue tracking Tracking failure detection Model Update Strategy Robotic minimally invasive surgery

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New generation of meteorology cameras 7

New generation of meteorology cameras

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Conference on Photonics, Devices, and systems vii

作者： Janout, Petr Blazek, Martin Pata, Petr Czech Tech Univ Fac Elect Engn Dept Radioelect Tech 2 Prague 16627 6 Czech Republic

ISBN: (数字)9781510617032

ISBN: (纸本)9781510617032;9781510617025

A new generation of the WILLIAM (WIde-field aLL-sky image Analyzing Monitoring system) camera includes new features such as monitoring of rain and storm clouds during the day observation. Development of the new generation of weather monitoring cameras responds to the demand for monitoring of sudden weather changes. However, new WILLIAM cameras are ready to process acquired image data immediately, release warning against sudden torrential rains, and send it to user's cell phone and email. Actual weather conditions are determined from image data, and results of image processing are complemented by data from sensors of temperature, humidity, and atmospheric pressure. In this paper, we present the architecture, image data processing algorithms of mentioned monitoring camera and spatially-variant model of imaging system aberrations based on Zernike polynomials.

关键词： Zernike polynomials meteorology image processing Ultra-wide-field of view Optics

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Fast algorithms and Architectures for 8-Point DST-II/DST-vii Approximations

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JOURNAL OF CIRCUITS systems AND COMPUTERS 2017年第3期26卷 1750045-1750045页

作者： Cintra, Renato J. Bayer, Fabio M. Madanayake, Arjuna Potluri, Uma S. Edirisuriya, Amila Univ Fed Pernambuco Signal Proc Grp Dept Stat Recife PE Brazil Inst Natl Sci Appl LIRIS Lyon France Univ Rennes 1 INRIA IRISA Equipe Cairn Rennes France Univ Fed Santa Maria Dept Stat Santa Maria RS Brazil Univ Fed Santa Maria LACESM Santa Maria RS Brazil Univ Akron ECE Akron OH 44325 USA

Multiplier-free fast algorithms are derived and analyzed for realizing the 8-point discrete sine transform of type II and type vii (DST-II and DST-vii) transforms with applications in image and video compression. A new fast algorithm is identified using numerical search methods for approximating DST-vii without employing multipliers. In addition, recently proposed fast algorithms for approximating the 8-point DCT-II are now extended to approximate DST-II. All proposed approximations for DST-II and DST-vii are compared with ideal transforms, and circuit complexity is measured using FPGA-based rapid prototypes on a 90nm Xilinx Virtex-4 device. The proposed architectures find applications in emerging video processing standards such as H.265/HEVC.

关键词： Approximate discrete transforms DCT DST fast algorithms FPGA low-complexity transform

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