Search results for "Machine learning"

showing 10 items of 1464 documents

Enhancement and assessment of WKS variance parameter for intelligent 3D shape recognition and matching based on MPSO

2016

This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method for extracting key-points from objects is introduced. Due to its discriminative power, the associated optimized WKS values with each point remain extremely stable, which allows for efficient salient features extraction. To assert our method regarding its robustness against topological deformations,…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO ] Computer Science [cs]Matching (graph theory)Feature vectorComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[INFO] Computer Science [cs][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Kernel (linear algebra)[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Discriminative modelRobustness (computer science)0202 electrical engineering electronic engineering information engineeringFeature (machine learning)[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]ComputingMilieux_MISCELLANEOUSMathematicsbusiness.industryParticle swarm optimization[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineeringPattern recognition020201 artificial intelligence & image processingArtificial intelligencebusinessEnergy (signal processing)
researchProduct

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]
researchProduct

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]
researchProduct

Automated uncertainty quantification analysis using a system model and data

2015

International audience; Understanding the sources of, and quantifying the magnitude of, uncertainty can improve decision-making and, thereby, make manufacturing systems more efficient. Achieving this goal requires knowledge in two separate domains: data science and manufacturing. In this paper, we focus on quantifying uncertainty, usually called uncertainty quantification (UQ). More specifically, we propose a methodology to perform UQ automatically using Bayesian networks (BN) constructed from three types of sources: a descriptive system model, physics-based mathematical models, and data. The system model is a high-level model describing the system and its parameters; we develop this model …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]generic modeling environment[SPI] Engineering Sciences [physics]Computer scienceuncertainty quantificationMachine learningcomputer.software_genre01 natural sciencesData modelingSystem model[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]010104 statistics & probability03 medical and health sciences[SPI]Engineering Sciences [physics][ SPI ] Engineering Sciences [physics]Sensitivity analysis0101 mathematicsUncertainty quantification[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]030304 developmental biologyautomation0303 health sciencesMathematical modelbusiness.industryConditional probabilityBayesian networkmeta-modelMetamodelingBayesian networkProbability distributionData miningArtificial intelligencebusinesscomputer
researchProduct

AN ONTOLOGY-BASED RECOMMENDER SYSTEM USING HIERARCHICAL MULTICLASSIFICATION FOR ECONOMICAL E-NEWS

2014

International audience; This paper focuses on a recommender system of economic news articles. Its objectives are threefold: (i) automatically multi-classify new economic articles, (ii) recommend articles by comparing profiles of users and multi-classification of articles, and (iii) managing the vocabulary of the economic news domain to improve the system based on seamlessly intervention of documentalists. In this paper we focus on the automatic multi-classification of the articles, managed by inference process of ontologies, and the enrichment of the documentalist-oriented ontology which provides the necessary capabilities to the DL reasoner for automatic multi-classification.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]recommender system[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]Multi-label classification[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO.INFO-IT ] Computer Science [cs]/Information Theory [cs.IT]machine learning[INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-IT] Computer Science [cs]/Information Theory [cs.IT][INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]e-newsontology[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]ComputingMilieux_MISCELLANEOUS
researchProduct

Advanced 3D movement analysis algorithms for robust functional capacity assessment.

2017

SummaryObjectives: We developed a novel system for in home functional capacities assessment in frail older adults by analyzing the Timed Up and Go movements. This system aims to follow the older people evolution, potentially allowing a forward detection of motor decompensation in order to trigger the implementation of rehabilitation. However, the pre-experimentations conducted on the ground, in different environments, revealed some problems which were related to KinectTM operation. Hence, the aim of this actual study is to develop methods to resolve these problems.Methods: Using the KinectTM sensor, we analyze the Timed Up and Go test movements by measuring nine spatio-temporal parameters, …

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]Computer science02 engineering and technologyTimed Up and Go testcomputer.software_genreCorrelation0302 clinical medicineHealth Information ManagementMICROSOFT KINECT0202 electrical engineering electronic engineering information engineering[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO][ SDV.IB ] Life Sciences [q-bio]/Bioengineeringsitting posture recognition[ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO]FALLSVideo processingPatient self-care home care and e-healthComputer Science Applications3D real-time video processing020201 artificial intelligence & image processing[SDV.IB]Life Sciences [q-bio]/BioengineeringAlgorithmsClinical testsCapacity assessmentGO TESTskin detectionFrail ElderlyMovementFrail Older AdultsPostureHealth InformaticsMachine learning03 medical and health sciencesRobustness (computer science)[ SDV.MHEP ] Life Sciences [q-bio]/Human health and pathologyclinical informaticsHumansVALIDITYOLDER-ADULTSSimulationAgedMonitoring Physiologicbusiness.industryMovement analysisMOTOR STRATEGIESArtificial intelligencebusinesscomputer030217 neurology & neurosurgery[SDV.MHEP]Life Sciences [q-bio]/Human health and pathologyApplied clinical informatics
researchProduct

Investigating the Relationship Between Community-aware and Classical Centrality Measures

2021

International audience

[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI][INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]ComputingMilieux_MISCELLANEOUS[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
researchProduct

An Empirical Comparison of Centrality and Hierarchy Measures in Complex Networks

2020

International audience

[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI][INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]ComputingMilieux_MISCELLANEOUS[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
researchProduct

Assessing the Relationship Between Centrality and Hierarchy in Complex Networks

2020

International audience

[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI][INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]ComputingMilieux_MISCELLANEOUS[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
researchProduct

Hierarchy and Centrality: Two Sides of The Same Coin?

2020

International audience

[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI][INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]ComputingMilieux_MISCELLANEOUS[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
researchProduct