Search results for "Pattern"

showing 10 items of 4203 documents

Reconnaissance de la Forme 3D et Estimation de la Profondeur Implémentation sur FPGA Spartan 3A d'un SoC pour la Vision 3D (Shape From Focus) Problém…

2007

Le terme de « vision 3D » ou « de numérisation 3D », est apparu à la fin des années 1990, pour désigner des techniques d'acquisition de mesures tridimensionnelle sur des surfaces, techniques ayant la caractéristique de donner des nuages de points denses et importants dont l'ordre de grandeur est de quelques dizaines à plusieurs millions de points. Le nuage de points représente en fait l'information de l'image de profondeur et selon des différents traitements à l'image on peut aboutir à un ordre de précision de la reconstitution de l'objet ou scène en 3D. La vision 3D demeure une méthodologie de base pour réassurer le mécanisme de reconstitution des images tridimensionnelles. Outre les besoi…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics[SPI.TRON] Engineering Sciences [physics]/Electronics[INFO.INFO-ES] Computer Science [cs]/Embedded Systems[INFO.INFO-PL] Computer Science [cs]/Programming Languages [cs.PL]
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Une approche performante de suivi visuel pour les caméras catadioptriques

2012

Session "Posters"; National audience; Dans cet article, nous proposons une méthode performante permettant d'appliquer des algorithmes de suivi visuel à des images catadioptriques. Cette méthode est basée sur une représentation sphérique de l'image qui permet de prendre en compte les distorsions et la résolution non-uniforme des images catadioptriques. Les résultats expérimentaux proposés démontrent que les méthodes probabilistes et déterministes peuvent être adaptées de manière à suivre un objet avec précision dans une séquence d'images catadioptriques

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]caméra catadioptrique[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]filtre particulaireSuivi visuel[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Mean-Shift[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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Optimisation et implémentation de méthodes bio-inspirées d'extraction de caractéristiques pour la reconnaissance d'objets visuels

2016

Industry has growing needs for so-called “intelligent systems”, capable of not only ac-quire data, but also to analyse it and to make decisions accordingly. Such systems areparticularly useful for video-surveillance, in which case alarms must be raised in case ofan intrusion. For cost saving and power consumption reasons, it is better to perform thatprocess as close to the sensor as possible. To address that issue, a promising approach isto use bio-inspired frameworks, which consist in applying computational biology modelsto industrial applications. The work carried out during that thesis consisted in select-ing bio-inspired feature extraction frameworks, and to optimize them with the aim t…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Bio-inspiréApprentissage automatiqueIntelligence artificielle[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Descripteurs[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]EmbarquéAlgorithm-architecture matching[ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM]Vision par ordinateurMachine learningRéseaux de neuronesComputer vision[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]OptimisationsFPGANeural networks[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
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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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Palmprint and face score level fusion: hardware implementation of a contactless small sample biometric system

2011

Including multiple sources of information in personal identity recognition and verification gives the opportunity to greatly improve performance. We propose a contactless biometric system that combines two modalities: palmprint and face. Hardware implementations are proposed on the Texas Instrument Digital Signal Processor and Xilinx Field-Programmable Gate Array (FPGA) platforms. The algorithmic chain consists of a preprocessing (which includes palm extraction from hand images), Gabor feature extraction, comparison by Hamming distance, and score fusion. Fusion possibilities are discussed and tested first using a bimodal database of 130 subjects that we designed (uB database), and then two …

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]Image fusion[INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR]BiometricsComputer sciencebusiness.industryFeature extractionGeneral EngineeringWord error rate020207 software engineeringImage processing02 engineering and technologyFacial recognition systemAtomic and Molecular Physics and OpticsMultimodal biometricsPattern recognition (psychology)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionArtificial intelligence[ INFO.INFO-AR ] Computer Science [cs]/Hardware Architecture [cs.AR]businessComputer hardware
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Whole mirror duplication-random loss model and pattern avoiding permutations

2010

International audience; In this paper we study the problem of the whole mirror duplication-random loss model in terms of pattern avoiding permutations. We prove that the class of permutations obtained with this model after a given number p of duplications of the identity is the class of permutations avoiding the alternating permutations of length p2+1. We also compute the number of duplications necessary and sufficient to obtain any permutation of length n. We provide two efficient algorithms to reconstitute a possible scenario of whole mirror duplications from identity to any permutation of length n. One of them uses the well-known binary reflected Gray code (Gray, 1953). Other relative mo…

[INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]Class (set theory)0206 medical engineeringBinary number0102 computer and information sciences02 engineering and technology[ MATH.MATH-CO ] Mathematics [math]/Combinatorics [math.CO]01 natural sciencesIdentity (music)Combinatorial problemsTheoretical Computer ScienceGray codeCombinatoricsPermutation[ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM]Gene duplicationRandom loss[MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO]Pattern avoiding permutationGenerating algorithmComputingMilieux_MISCELLANEOUSMathematicsDiscrete mathematicsWhole duplication-random loss modelMathematics::CombinatoricsGenomeParity of a permutationComputer Science Applications[MATH.MATH-CO] Mathematics [math]/Combinatorics [math.CO][ INFO.INFO-CC ] Computer Science [cs]/Computational Complexity [cs.CC]Binary reflected Gray code010201 computation theory & mathematicsSignal Processing[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]020602 bioinformaticsAlgorithmsInformation Systems
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QaQ: Robust 6D Pose Estimation via Quality-Assessed RGB-D Fusion

2023

RGB-D 6D pose estimation has recently drawn great research attention thanks to the complementary depth information. Whereas, the depth and the color image are often noisy in real industrial scenarios. Therefore, it becomes challenging for many existing methods that fuse equally RGB and depth features. In this paper, we present a novel fusion design to adaptively merge RGB-D cues. Specifically, we created a Qualityassessment block that estimates the global quality of the input modalities. This quality represented as an α parameter is then used to reinforce the fusion. We have thus found a simple and effective way to improve the robustness to low-quality inputs in terms of Depth and RGB. Exte…

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
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3D dimensional measurement of large hot metallic shells

2009

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
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3D laser system for shell dimension measurement during forging

2008

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
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Détermination de la texture de la feuille de vigne par imagerie

2013

National audience; Dans le contexte de la pulvérisation de précision, nombreuses sont les recherches menées sur l'optimisation d'utilisation des produits phytosanitaires. L'objectif final étant de réduire de manière significative la quantité d'intrant dans les cultures . Dans ce cadre, les travaux présentés dans cet article s'intéresse particulièrement à l'analyse de l'état de surface foliaire qui présente une part essentielle dans le processus d'adhésion du produit pulvérisé sur la feuille. L'analyse de surface de la feuille est réalisée à travers l'analyse des caractéristiques texturale extraites d'images microscopics. Afin de discriminer les différents cépages et âges des feuilles retenu…

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Analyse discriminante linéaire et non linéaire[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]TextureDescripteur Généralise de FourierRéseau de neuronessurface foliaire[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
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