Search results for " image processing."

showing 10 items of 2265 documents

Incorporating depth information into few-shot semantic segmentation

2021

International audience; Few-shot segmentation presents a significant challengefor semantic scene understanding under limited supervision.Namely, this task targets at generalizing the segmentationability of the model to new categories given a few samples.In order to obtain complete scene information, we extend theRGB-centric methods to take advantage of complementary depthinformation. In this paper, we propose a two-stream deep neuralnetwork based on metric learning. Our method, known as RDNet,learns class-specific prototype representations within RGB anddepth embedding spaces, respectively. The learned prototypesprovide effective semantic guidance on the corresponding RGBand depth query ima…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Artificial neural networkComputer sciencebusiness.industry[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020206 networking & telecommunications02 engineering and technologyImage segmentationSemanticsVisualization[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 ProcessingMetric (mathematics)0202 electrical engineering electronic engineering information engineeringEmbeddingRGB color modelSegmentationComputer visionArtificial intelligencebusiness
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Unsupervised learning of category-specific symmetric 3D keypoints from point sets

2020

Lecture Notes in Computer Science, 12370

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]FOS: Computer and information sciencesComputer sciencePlane symmetryComputer Vision and Pattern Recognition (cs.CV)Point cloudComputer Science - Computer Vision and Pattern Recognition02 engineering and technology010501 environmental sciences01 natural sciences[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Linear basis0202 electrical engineering electronic engineering information engineeringComputingMilieux_COMPUTERSANDEDUCATIONPoint (geometry)0105 earth and related environmental sciencesbusiness.industryCategory specific[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Pattern recognition16. Peace & justiceBenchmark (computing)Unsupervised learning020201 artificial intelligence & image processingArtificial intelligenceSymmetry (geometry)business
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Enhancing scientific information systems with semantic annotations

2013

International audience; Scientific Information Systems aim to produce or improve knowledge on a subject through activities of research and development. The management of scientific dat a requires some essential properties. We propose SemLab an architecture that sup ports interoperability, data quality and extensibility through a unique paradigm: semantic annotation. We present two app lications that validate our architecture.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Information retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceInteroperability[INFO.INFO-WB]Computer Science [cs]/Web[ INFO.INFO-WB ] Computer Science [cs]/WebSubject (documents)02 engineering and technologyOntology (information science)Semantic interoperabilityData science[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]020204 information systemsData qualitySemantic computing0202 electrical engineering electronic engineering information engineeringInformation system[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]020201 artificial intelligence & image processing[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]
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Representing and Reasoning for Spatiotemporal Ontology Integration

2004

International audience; The World-Wide Web hosts many autonomous and heterogeneous information sources. In the near future each source may be described by its own ontology. The distributed nature of ontology development will lead to a large number of local ontologies covering overlapping domains. Ontology integration will then become an essential capability for effective interoperability and information sharing. Integration is known to be a hard problem, whose complexity increases particularly in the presence of spatiotemporal information. Space and time entail additional problems such as the heterogeneity of granularity used in representing spatial and temporal features. Spatio-temporal ob…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Information retrieval[INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO]Computer scienceOntologyProcess ontologyOntology-based data integrationSuggested Upper Merged OntologyIntegration[INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]Spatio-Temporal data02 engineering and technologyOntology (information science)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Open Biomedical OntologiesMapping020204 information systemsOntology components0202 electrical engineering electronic engineering information engineeringUpper ontology020201 artificial intelligence & image processingOntology alignment
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Improving Video Object Detection by Seq-Bbox Matching

2019

International audience

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Matching (statistics)business.industryComputer science02 engineering and technology010501 environmental sciences01 natural sciencesObject detection[INFO.INFO-ES] Computer Science [cs]/Embedded Systems[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer vision[INFO.INFO-ES]Computer Science [cs]/Embedded SystemsArtificial intelligencebusinessComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciences
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Event-Based Trajectory Prediction Using Spiking Neural Networks

2021

International audience; In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]PolynomialComputer scienceNeuroscience (miscellaneous)Neurosciences. Biological psychiatry. Neuropsychiatry02 engineering and technologyunsupervised learningSNN[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]STDP03 medical and health sciencesCellular and Molecular Neuroscience0302 clinical medicineLearning rule0202 electrical engineering electronic engineering information engineeringEvent (probability theory)Original ResearchSpiking neural networkQuantitative Biology::Neurons and Cognitionmotion selectivitybusiness.industry[SCCO.NEUR]Cognitive science/Neuroscience[SCCO.NEUR] Cognitive science/NeuroscienceProcess (computing)Pattern recognitionspiking cameraTrajectoryball trajectory predictionUnsupervised learning020201 artificial intelligence & image processingArtificial intelligencebusiness030217 neurology & neurosurgeryEfficient energy useNeuroscienceRC321-571Frontiers in Computational Neuroscience
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Some Computational Aspects of DISTANCE-SAT

2007

In many AI fields, one must face the problem of finding a solution that is as close as possible to a given configuration. This paper addresses this problem in a propositional framework. We introduce the decision problem distance-sat, which consists in determining whether a propositional formula admits a model that disagrees with a given partial interpretation on at most d variables. The complexity of distance-sat and of several restrictions of it are identified. Two algorithms based on the well-known Davis/Logemann/Loveland search procedure for the satisfiability problem sat are presented so as to solve distance-sat for CNF formulas. Their computational behaviors are compared with the ones …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Theoretical computer scienceComputational complexity theory0102 computer and information sciences02 engineering and technologyComputer Science::Computational Complexity01 natural sciences[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]#SATArtificial IntelligenceComputer Science::Logic in Computer ScienceDPLL algorithm0202 electrical engineering electronic engineering information engineeringComputingMilieux_MISCELLANEOUSMathematicsDecision problemFunction problemSatisfiabilityPropositional formulaTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESComputational Theory and Mathematics010201 computation theory & mathematics020201 artificial intelligence & image processingBoolean satisfiability problemAlgorithmSoftware
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How to Enrich Description Logics with Fuzziness

2017

International audience; The paper describes the relation between fuzzy and non-fuzzy description logics. It gives an overview about current research in these areas and describes the difference between tasks for description logics and fuzzy logics. The paper also deals with the transformation properties of description logics to fuzzy logics and backwards. While the process of transformation from a description logic to a fuzzy logic is a trivial inclusion, the other way of reducing information from fuzzy logic to description logic is a difficult task, that will be topic of future work.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Theoretical computer science[ INFO ] Computer Science [cs]Relation (database)Process (engineering)Computer scienceMathematics::General Mathematics0102 computer and information sciences02 engineering and technology[INFO] Computer Science [cs]01 natural sciencesFuzzy logicTask (project management)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Knowledge-based systemsFuzzy Description LogicDescription logicComputer Science::Logic in Computer Science0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Semantic WebSemantic WebUncertaintyTransformation (function)TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES010201 computation theory & mathematics020201 artificial intelligence & image processingComputingMethodologies_GENERALHardware_LOGICDESIGN
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Propagation d'informations le long d'une ligne de transmission non linéaire structurée en super réseau et simulant un neurone myélinisé

2019

Non-linear systems are almostly described by partial differential equations that characterize them. We have some systems such as the chain of coupled pebdelums, the protein chain comprising molecules with hydrogen bonds, atomic lattice, and so on .These systems are most often characterized by anharmonic inter particulate interactions and and then immersed in deformable potential substrates. In addition to nonlinearity and dispersion, these other phenomena namely anharmonicity and deformability are responsible for certain properties of propagation of solitary waves such as (compactons, kinks and anti-kinks, peackons, ...etc) and also the ability of the systems to transmit a signal . We used …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]TransmitivitySoliton solutionsEquations aux dérivées partiellesTransmissivitéPartial differential equations[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSimulationSolution soliton[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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Customizing Semantic Profiling for Digital Advertising

2014

International audience; Personalization is the new magic buzzword of application development. To make the complexity of today's application functionalities and information spaces "digestible", customization has become the new go-to technique. But while those technologies aim to ease the consumption of media for their users, they suffer from the same problematic: in the age of Big Data, applications have to cope with a conundrum of heterogeneous information sources that have to be perceived, processed and interpreted. Researchers tend to aim for a maximum degree of integration to create the perfect, all-embracing personalization. The results are wide-range, but overly complex systems that su…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO ] Computer Science [cs]Computer scienceBig dataComplex systemsemantic technologies02 engineering and technology[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]Personalization[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]World Wide Web020204 information systems0202 electrical engineering electronic engineering information engineeringProfiling (information science)Heterogeneous information[ INFO.INFO-CL ] Computer Science [cs]/Computation and Language [cs.CL][INFO]Computer Science [cs]user profiles[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]OWLuser profilingbusiness.industryScalabilitySemantic technology020201 artificial intelligence & image processingbusinessDigital advertisingcustomization
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