Search results for "processing"

showing 10 items of 8572 documents

Approche ontologique pour l'analyse de données spatiales, Journée Scientifique Pluridisciplinaire, Traitements Statistiques des Données Spatiales

2014

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO][INFO.INFO-WB] Computer Science [cs]/Web[INFO.INFO-TT] Computer Science [cs]/Document and Text Processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]
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HIGEOMES : bilan et perspectives

2014

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO][INFO.INFO-WB] Computer Science [cs]/Web[INFO.INFO-TT] Computer Science [cs]/Document and Text Processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]
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Usage de la sémantique : des services de catalogue à l’analyse des phénomènes dynamiques

2014

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO][INFO.INFO-WB] Computer Science [cs]/Web[INFO.INFO-TT] Computer Science [cs]/Document and Text Processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]
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Deep learning for dehazing: Benchmark and analysis

2018

International audience; We compare a recent dehazing method based on deep learning , Dehazenet, with traditional state-of-the-art approach, on benchmark data with reference. Dehazenet estimates the depth map from a single color image, which is used to inverse the Koschmieder model of imaging in the presence of haze. In this sense, the solution is still attached to the Koschmieder model. We demonstrate that this method exhibits the same limitation than other inversions of this imaging model.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][STAT.ML] Statistics [stat]/Machine Learning [stat.ML][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[STAT.ML]Statistics [stat]/Machine Learning [stat.ML][ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI][ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML][ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]
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Design and Calibration of an Omni-RGB plus D Camera

2016

International audience; In this paper, we present the design of a new camera combining both predator-like and prey-like vision features. This setup provides both a spherical RGB-view and a directional depth-view of the environment. The model and calibration of the full set-up are described. A few examples will be given to demonstrate the interest and the versatility of such camera for robotics and video surveillance at the oral presentation.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO][ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO]ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONstereo vision[SPI.TRON] Engineering Sciences [physics]/Electronics[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][SPI.TRON]Engineering Sciences [physics]/Electronicsfisheye[ SPI.TRON ] Engineering Sciences [physics]/Electronics[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]dioptric[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]unified model
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An Ontology-Based Approach for the Reconstruction and Analysis of Digital Incidents Timelines

2015

International audience; Due to the democratisation of new technologies, computer forensics investigators have to deal with volumes of data which are becoming increasingly large and heterogeneous. Indeed, in a single machine, hundred of events occur per minute, produced and logged by the operating system and various software. Therefore, the identification of evidence, and more generally, the reconstruction of past events is a tedious and time-consuming task for the investigators. Our work aims at reconstructing and analysing automatically the events related to a digital incident, while respecting legal requirements. To tackle those three main problems (volume, heterogeneity and legal require…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceOntology PopulationDigital forensics[INFO.INFO-OH]Computer Science [cs]/Other [cs.OH][ INFO.INFO-WB ] Computer Science [cs]/Web02 engineering and technologyEvent ReconstructionOntology (information science)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]SoftwareKnowledge extraction[INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY]020204 information systemsForensic OntologyTimeline Analysis0202 electrical engineering electronic engineering information engineering[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Event reconstructionKnowledge Extractionbusiness.industry[INFO.INFO-WB]Computer Science [cs]/WebTimelineComputer forensicsData scienceComputer Science Applications[ INFO.INFO-CY ] Computer Science [cs]/Computers and Society [cs.CY][INFO.INFO-OH] Computer Science [cs]/Other [cs.OH]Medical Laboratory TechnologyIdentification (information)Digital Forensics[INFO.INFO-CY] Computer Science [cs]/Computers and Society [cs.CY][ INFO.INFO-OH ] Computer Science [cs]/Other [cs.OH]020201 artificial intelligence & image processingbusinessLaw
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Architectural Reconstruction of 3D Building Objects through Semantic Knowledge Management

2010

International audience; This paper presents an ongoing research which aims at combining geometrical analysis of point clouds and semantic rules to detect 3D building objects. Firstly by applying a previous semantic formalization investigation, we propose a classification of related knowledge as definition, partial knowledge and ambiguous knowledge to facilitate the understanding and design. Secondly an empirical implementation is conducted on a simplified building prototype complying with the IFC standard. The generation of empirical knowledge rules is revealed and semantic scopes are addressed both in the bottom up manner along the line of geometry --> topology --> semantic, and a vice ver…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]cognitionComputer science02 engineering and technologySemanticscomputer.software_genreSocial Semantic Webformal[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Semantic similaritySemantic computing0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Semantic WebInformation retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]Semantic Web Rule Languagebusiness.industryepistemology020207 software engineeringknowledge management[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]Semantic gridsemanticSemantic technology020201 artificial intelligence & image processingData miningbusinesscomputer
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Ontology-based Integration of Web Navigation for Dynamic User Profiling

2015

The development of technology for handling information on a Big Data-scale is a buzzing topic of current research. Indeed, improved techniques for knowledge discovery are crucial for scientific and economic exploitation of large-scale raw data. In research collaboration with an industrial actor, we explore the applicability of ontology-based knowledge extraction and representation for today's biggest source of large-scale data, the Web. The goal is to develop a profiling application, based on the implicit information that every user leaves while navigating the online, with the goal to identify and model preferences and interests in a detailed user profile. This includes the identification o…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]lcsh:Computer engineering. Computer hardware[ INFO ] Computer Science [cs]Knowledge representation and reasoningComputer scienceSemantic Web Ontologies SWRL Big Data reasoningBig datalcsh:TK7885-789502 engineering and technologyOntology (information science)[INFO] Computer Science [cs][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Big Data reasoningWorld Wide WebKnowledge extraction020204 information systems0202 electrical engineering electronic engineering information engineeringOntologiesWeb navigation[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Semantic WebSWRLSemantic WebUser profilebusiness.industrylcsh:Zlcsh:Bibliography. Library science. Information resourcesSemantic technology020201 artificial intelligence & image processingbusiness
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Une architecture programmable de traitement des impulsions zéro-temps mort pour l'instrumentation nucléaire

2015

In the field of nuclear instrumentation, digital signal processing architectures have to deal with the poissonian characteristic of the signal, composed of random arrival pulses which requires current architectures to work in dataflow. Thus, the real-time needs implies losing pulses when the pulse rate is too high. Current architectures paralyze the acquisition of the signal during the pulse processing inducing a time during no signal can be processed, this is called the dead time. These issue have led current architectures to use dedicated solutions based on reconfigurable components such as FPGAs. The requirement of end users to implement a wide range of applications on a large number of …

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]Architecture électroniqueInstrumentation nucléaireRadioactivité[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingDigital Signal Processing (DSP)traitement du signalNuclear instrumentation[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]Distributed computing[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingTraitement numérique du signal (TNS)Électronique numériqueMesureArchitecture électronique distribuée[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]Digital Pulse Processing (DPP)signal processingTraitement numérique des impulsions (DPP)
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Inverse Tone Mapping Based upon Retina Response

2014

International audience; The development of high dynamic range (HDR) display arouses the research of inverse tone mapping methods, which expand dynamic range of the low dynamic range (LDR) image to match that of HDR monitor. This paper proposed a novel physiological approach, which could avoid artifacts occurred in most existing algorithms. Inspired by the property of the human visual system (HVS), this dynamic range expansion scheme performs with a low computational complexity and a limited number of parameters and obtains high-quality HDR results. Comparisons with three recent algorithms in the literature also show that the proposed method reveals more important image details and produces …

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]Computational complexity theoryArticle SubjectComputer sciencemedia_common.quotation_subjectComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONlcsh:MedicineTone mappinglcsh:TechnologyRetinaGeneral Biochemistry Genetics and Molecular BiologyImage (mathematics)BiomimeticsDistortionImage Interpretation Computer-AssistedHumansContrast (vision)Computer visionlcsh:ScienceHigh dynamic rangeGeneral Environmental Sciencemedia_commonDynamic rangebusiness.industrylcsh:Tlcsh:RGeneral MedicineImage EnhancementHuman visual system modellcsh:QArtificial intelligence[ INFO.INFO-AR ] Computer Science [cs]/Hardware Architecture [cs.AR]businessAlgorithmsColor PerceptionResearch ArticleThe Scientific World Journal
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