Search results for "Image"

showing 10 items of 6818 documents

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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ROBUST ROAD SIGNS SEGMENTATION IN COLOR IMAGES

2012

International audience; This paper presents an efficient method for road signs segmentation in color images. Color segmentation of road signs is a difficult task due to variations in the image acquisition conditions. Therefore, a color constancy algorithm is usually applied prior to segmentation, which increases the computation time. The proposed method is based on a log-chromaticity color space which shows good invariance properties to changing illumination. Thus, the method is simple and fast since it does not require color constancy algorithms. Experiments with a large dataset and comparison with other approaches, show the robustness and accuracy of the method in detecting road signs in …

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Color segmentationRoad sign detectionLog-chromaticity color space.ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Log-chromaticity color space[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Color constancy
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Background subtraction with multispectral video sequences

2014

International audience; Motion analysis of moving targets is an important issue in several applications such as video surveillance or robotics. Background subtraction is one of the simplest and widely used techniques for moving target detection in video sequences. In this paper, we investigate the advantages of using a multispectral video acquisition system of more than three bands for background subtraction over the use of trichromatic or monochromatic video sequences. To this end, we have established a dataset of multispectral videos with a manual annotation of moving objects. To the best of our knowledge, this is the first publicly available dataset of multispectral video sequences. Expe…

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]ComputingMethodologies_PATTERNRECOGNITIONComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[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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An SVD-Based Approach for Ghost Detection and Removal in High Dynamic Range Images

2012

International audience; In this paper, we propose a simple method for the ghost detection problem in the context of merging multiple low dynamic range (LDR) images to form a high dynamic range (HDR) image. We show that the second biggest singular values extracted over local spatio-temporal neighbourhoods can be effectively used for ghost region detection. Furthermore, we combine the proposed method with an exposure fusion technique to generate final HDR image free of ghosting artefacts. We present experimental results to illustrate the efficiency of the proposed method and quantitative comparison with other existing approaches show the good performance of our method in detecting and removin…

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]HDR ImagesGhost detectionHigh Energy Physics::LatticeComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[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]SVDGeneralLiterature_MISCELLANEOUS
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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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