Search results for "image processing"

showing 10 items of 3285 documents

Multi-Kernel Implicit Curve Evolution for Selected Texture Regions Segmentation in VHR Satellite Images

2014

Very high resolution (VHR) satellite images provide a mass of detailed information which can be used for urban planning, mapping, security issues, or environmental monitoring. Nevertheless, the processing of this kind of image is timeconsuming, and extracting the needed information from among the huge quantity of data is a real challenge. For some applications such as natural disaster prevention and monitoring (typhoon, flood, bushfire, etc.), the use of fast and effective processing methods is demanded. Furthermore, such methods should be selective in order to extract only the information required to allow an efficient interpretation. For this purpose, we propose a texture region segmentat…

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR][INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR]Pixelbusiness.industryComputer science0211 other engineering and technologiesGraphics processing unitBoundary (topology)Scale-space segmentation02 engineering and technologyImage segmentationFuzzy logicImage texture11. Sustainability0202 electrical engineering electronic engineering information engineeringGeneral Earth and Planetary Sciences020201 artificial intelligence & image processingComputer visionSegmentation[ INFO.INFO-AR ] Computer Science [cs]/Hardware Architecture [cs.AR]Artificial intelligenceElectrical and Electronic EngineeringbusinessComputingMilieux_MISCELLANEOUS021101 geological & geomatics engineering
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PROCEDE DE PRE-DISTORSION NUMERIQUE D’UN SIGNAL ET REPETEUR DE TELECOMMUNICATION INTEGRANT UN FILTRE A REPONSE IMPULSIONNELLE FINIE POUR METTRE EN OE…

2013

L'invention concerne un procédé de pré-distorsion numérique d'un signal de télécommunication traité dans un circuit électronique 100 intégrant un filtre à réponse impulsionnelle finie 321. Ce procédé consiste successivement: - à identifier, à la sortie du circuit 100, les paramètres de distorsions de phase et/ou d'amplitude du signal en fonction de la fréquence, - à partir des susdits paramètres de distorsions relevés, à générer, par un algorithme basé sur une interpolation, des coefficients permettant d'effectuer dans ledit filtre 321, des prédistorsions du signal numérique destinées à engendrer une précorrection des susdites distorsions, - à transférer lesdits coefficients de pré-distorsi…

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR][SPI.OTHER]Engineering Sciences [physics]/Other[INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR][ SPI.OTHER ] Engineering Sciences [physics]/Other[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]Prédistorsion numérique[ INFO.INFO-ES ] Computer Science [cs]/Embedded SystemsRépéteurs[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS][ INFO.INFO-SI ] Computer Science [cs]/Social and Information Networks [cs.SI][SPI.OTHER] Engineering Sciences [physics]/Other[INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI]Spline[SPI.TRON] Engineering Sciences [physics]/Electronics[INFO.INFO-ES] Computer Science [cs]/Embedded Systems[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/Electronics[ INFO.INFO-MS ] Computer Science [cs]/Mathematical Software [cs.MS][INFO.INFO-MS] Computer Science [cs]/Mathematical Software [cs.MS]FIR filters[INFO.INFO-ES]Computer Science [cs]/Embedded Systems[ INFO.INFO-AR ] Computer Science [cs]/Hardware Architecture [cs.AR][SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingFpga
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Scheduling independent stochastic tasks on heterogeneous cloud platforms

2019

International audience; This work introduces scheduling strategies to maximize the expected number of independent tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The cloud platform is composed of several types of virtual machines (VMs), where each type has a unitexecution cost that depends upon its characteristics. The amount of budget spent during the execution of a task on a given VM is the product of its execution length by the unit execution cost of that VM. The execution lengths of tasks follow a variety of standard probability distributions (exponential, uniform, halfnormal, etc.), which is known beforehand and whose mean and stand…

[INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]020203 distributed computingComputer scienceStochastic processbusiness.industryDistributed computing[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]Processor schedulingCloud computing02 engineering and technologycomputer.software_genreScheduling (computing)Virtual machine0202 electrical engineering electronic engineering information engineeringTask analysisProbability distribution020201 artificial intelligence & image processing[INFO]Computer Science [cs][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]InterruptHeuristicsbusinesscomputer
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Scheduling independent stochastic tasks under deadline and budget constraints

2018

This article discusses scheduling strategies for the problem of maximizing the expected number of tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The execution times of tasks follow independent and identically distributed probability laws. The main questions are how many processors to enroll and whether and when to interrupt tasks that have been executing for some time. We provide complexity results and an asymptotically optimal strategy for the problem instance with discrete probability distributions and without deadline. We extend the latter strategy for the general case with continuous distributions and a deadline and we design an ef…

[INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]Mathematical optimizationOperations researchComputer science[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]Cloud computing[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]02 engineering and technologyExpected valueTheoretical Computer ScienceScheduling (computing)[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]deadline0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]schedulingComputer Science::Operating SystemsComputingMilieux_MISCELLANEOUSBudget constraint020203 distributed computingcloud platformindependent tasksbusiness.industry[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulationstochastic costAsymptotically optimal algorithmContinuous distributions[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Hardware and ArchitectureProbability distribution[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]020201 artificial intelligence & image processingInterrupt[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]businessSoftwarebudget
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Learning Bag of Spatio-Temporal Features for Human Interaction Recognition

2019

Bag of Visual Words Model (BoVW) has achieved impressive performance on human activity recognition. However, it is extremely difficult to capture high-level semantic meanings behind video features with this method as the spatiotemporal distribution of visual words is ignored, preventing localization of the interactions within a video. In this paper, we propose a supervised learning framework that automatically recognizes high-level human interaction based on a bag of spatiotemporal visual features. At first, a representative baseline keyframe that captures the major body parts of the interacting persons is selected and the bounding boxes containing persons are extracted to parse the poses o…

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Sum of HistogramsBag of Visual WordsHuman interaction[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingSVMEdge-based regionMSER3D-SIFT
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Proc. of SPIE, Optics, Photonics, and Digital Technologies for Multimedia Applications II

2012

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]imaging[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingoptics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingimage processing
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Détection de la dépression par l’analyse de la géométrie faciale et de la parole

2017

Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depression. The proposed system has been tested both in gender independent and gender based modes, and with different fusion methods. The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. The proposed framework achieved a precision of 94.8% for detecting persons achieving high sc…

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image Processing[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
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Online Multi-object Tracking Combining Optical Flow and Compressive Tracking for Intelligent Vehicles

2017

International audience; no abstract

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][SPI.AUTO] Engineering Sciences [physics]/Automatic[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][ SPI.AUTO ] Engineering Sciences [physics]/Automatic[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image Processing[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingComputingMilieux_MISCELLANEOUS[SPI.AUTO]Engineering Sciences [physics]/Automatic[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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Hybrid Model/data-Driven Fault Detection and Exclusion for a Decentralized Cooperative Multi-Robot System

2022

International audience

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][SPI.AUTO] Engineering Sciences [physics]/Automatic[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO][INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[SPI.AUTO]Engineering Sciences [physics]/Automatic
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Using Visual Saliency for Object Tracking with Particle Filters

2010

International audience; This paper presents a robust tracking method based on the integration of visual saliency information into the particle filter framework. While particle filter has been successfully used for tracking non-rigid objects, it shows poor performances in the presence of large illumination variation, occlusions and when the target object and background have similar color distributions. We show that considering saliency information significantly improves the performance of particle filter based tracking. In particular, the proposed method is robust against occlusion and large illumination variation while requiring a reduced number of particles. Experimental results demonstrate th…

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]particle filters0202 electrical engineering electronic engineering information engineeringComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineering020201 artificial intelligence & image processing02 engineering and technologytracking[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]ComputingMethodologies_COMPUTERGRAPHICSvisual saliency
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