Search results for " Resolution [1373 (2001)]"

showing 4 items of 4 documents

Learning User's Confidence for Active Learning

2013

In this paper, we study the applicability of active learning in operative scenarios: more particularly, we consider the well-known contradiction between the active learning heuristics, which rank the pixels according to their uncertainty, and the user's confidence in labeling, which is related to both the homogeneity of the pixel context and user's knowledge of the scene. We propose a filtering scheme based on a classifier that learns the confidence of the user in labeling, thus minimizing the queries where the user would not be able to provide a class for the pixel. The capacity of a model to learn the user's confidence is studied in detail, also showing the effect of resolution is such a …

FOS: Computer and information sciencesComputer Science - Machine LearningActive learning (machine learning)Computer scienceComputer Vision and Pattern Recognition (cs.CV)SVM0211 other engineering and technologiesComputer Science - Computer Vision and Pattern RecognitionContext (language use)02 engineering and technologyMachine learningcomputer.software_genreTask (project management)Machine Learning (cs.LG)Classifier (linguistics)0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineeringbad statesElectrical and Electronic Engineeringphotointerpretationuser's confidence021101 geological & geomatics engineeringActive learning (AL)Pixelbusiness.industryRank (computer programming)Image and Video Processing (eess.IV)very high resolution (VHR) imagery020206 networking & telecommunicationsElectrical Engineering and Systems Science - Image and Video ProcessingClass (biology)General Earth and Planetary SciencesArtificial intelligenceHeuristicsbusinesscomputerIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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A survey of active learning algorithms for supervised remote sensing image classification

2011

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active …

FOS: Computer and information sciencesComputer scienceComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionMachine learningcomputer.software_genreactive learningHyperspectral image classificationEntropy (information theory)Electrical and Electronic EngineeringArchitectureRemote sensingvery high resolution (VHR)PixelContextual image classificationbusiness.industryHyperspectral imagingSupport vector machinehyperspectraltraining set definitionSignal Processingsupport vector machine (SVM)Artificial intelligenceHeuristicsbusinessAlgorithmcomputerimage classification
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Integration of high resolution spatial and spectral data acquisition systems to provide complementary datasets for cultural heritage applications

2010

International audience; Modern optical measuring systems are able to record objects with high spatial and spectral precision. The acquisition of spatial data is possible with resolutions of a few hundredths of a millimeter using active projection-based camera systems, while spectral data can be obtained using filter-based multispectral camera systems that can capture surface spectral reflectance with high spatial resolution. We present a methodology for combining data from these two discrete optical measuring systems by registering their individual measurements into a common geometrical frame. Furthermore, the potential for its application as a tool for the non-invasive monitoring of painti…

data fusion[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processinghigh resolution 3D datalaser scanning[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingcultural heritagemultispectral acquisitionsphotogrammetrysurface analysisimage registrationstone conservation[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processinghigh resolution 3D data.[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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Intervención en Libia: La responsabilidad de proteger a debate

2011

Resumen El trabajo analiza el origen y evolución del concepto de la responsabilidad de proteger, sus antecedentes en la década de los noventa del S. XX, su formulación en el Informe sobre La responsabilidad de proteger,  y su consolidación posterior con especial atención al Documento Final de la Cumbre de 2005. Se examina asimismo el modo en que se ha aplicado esta norma emergente en la Resolución 1973 del Consejo de Seguridad, de 17 de marzo de 2011, que ha autorizado el uso de la fuerza en Libia. Y se concluye que casi todas las limitaciones y objeciones que se están planteando en relación con esta intervención, probablemente son consustanciales al modo en que actualmente se ponen en prác…

ius cogens normsResolución 1973 del Consejo de Seguridadlcsh:Jurisprudence. Philosophy and theory of lawHumanitarian interventionK201-487Intervención humanitariaIntervención humanitaria; autoridad competente; normas de ius cogens; Resolución 1973 del Consejo de Seguridad; responsabilidad de prevenir; Humanitarian intervention; right authority; ius cogens norms; Security Council Resolution 1973; responsibilitynormas de ius cogensright authorityresponsabilidad de prevenirSecurity Council Resolution 1973Jurisprudence. Philosophy and theory of lawresponsibilitylcsh:K201-487autoridad competente
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