Search results for "texture"

showing 10 items of 402 documents

Analysis of HMAX Algorithm on Black Bar Image Dataset

2020

An accurate detection and classification of scenes and objects is essential for interacting with the world, both for living beings and for artificial systems. To reproduce this ability, which is so effective in the animal world, numerous computational models have been proposed, frequently based on bioinspired, computational structures. Among these, Hierarchical Max-pooling (HMAX) is probably one of the most important models. HMAX is a recognition model, mimicking the structures and functions of the primate visual cortex. HMAX has already proven its effectiveness and versatility. Nevertheless, its computational structure presents some criticalities, whose impact on the results has never been…

Computer Networks and CommunicationsComputer sciencelcsh:TK7800-8360Context (language use)02 engineering and technologySet (abstract data type)03 medical and health sciences0302 clinical medicineGabor filterBBIDEncoding (memory)0202 electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringModularity (networks)Contextual image classificationbusiness.industrylcsh:ElectronicsPattern recognitioncomputational modelBlack Bar Image DatasetHardware and ArchitectureControl and Systems EngineeringHMAXSignal Processingtexture classification020201 artificial intelligence & image processingArtificial intelligencerecognitionbusiness030217 neurology & neurosurgeryimage classificationElectronics
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Complex networks : application for texture characterization and classification

2008

This article describes a new method and approch of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we presente how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditionnal and extended hierarchical measurements, are used to characterize ”organisation” of textures.

Computer engineering. Computer hardwareTexture compressionComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONComplex networksImage processingTexture (geology)TK7885-7895Image textureImage processingAnàlisi de texturaProcesamiento de imágenestexture analysisClustering coefficientAnálisis de texturaRedes complejasPixelbusiness.industryNode (networking)Pattern recognitionProcessament d'imatgescomplex networksQA75.5-76.95Xarxes complexesComplex networkTexture analysisElectronic computers. Computer scienceComputer Science::Computer Vision and Pattern RecognitionComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftwareELCVIA: electronic letters on computer vision and image analysis
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A new image segmentation approach using community detection algorithms

2015

Image segmentation has an important role in many image processing applications. Several methods exist for segmenting an image. However, this technique is still a relatively open topic for which various research works are regularly presented. With the recent developments on complex networks theory, image segmentation techniques based on graphs has considerably improved. In this paper, we present a new perspective of image segmentation, by applying three of the most efficient community detection algorithms, Louvain, infomap and stability optimization based on the louvain algorithm, and we extract communities in which the highest modularity feature is achieved. After we show that this measure …

Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationImage processing02 engineering and technology[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]03 medical and health sciences0302 clinical medicine[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]Image textureMinimum spanning tree-based segmentation020204 information systems0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]Computer visionSegmentationComputingMilieux_MISCELLANEOUSbusiness.industrySegmentation-based object categorization[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]Pattern recognitionImage segmentationRegion growingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingAlgorithm030217 neurology & neurosurgery2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)
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Image Inpainting Methods Evaluation and Improvement

2014

With the upgrowing of digital processing of images and film archiving, the need for assisted or unsupervised restoration required the development of a series of methods and techniques. Among them, image inpainting is maybe the most impressive and useful. Based on partial derivative equations or texture synthesis, many other hybrid techniques have been proposed recently. The need for an analytical comparison, beside the visual one, urged us to perform the studies shown in the present paper. Starting with an overview of the domain, an evaluation of the five methods was performed using a common benchmark and measuring the PSNR. Conclusions regarding the performance of the investigated algorith…

Computer scienceInpaintinglcsh:MedicineImage processingReview Articlelcsh:TechnologyGeneral Biochemistry Genetics and Molecular BiologyDomain (software engineering)Image (mathematics)Pattern Recognition AutomatedDigital image processingImage Interpretation Computer-AssistedImage Processing Computer-AssistedComputer visionlcsh:ScienceGeneral Environmental Sciencebusiness.industrylcsh:Tlcsh:RGeneral MedicineBenchmark (computing)Partial derivativelcsh:QArtificial intelligencebusinessAlgorithmsTexture synthesisThe Scientific World Journal
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Dynamic 3D Scene Reconstruction and Enhancement

2017

International audience; In this paper, we present a 3D reconstruction and enhancement approach for high quality dynamic city scene reconstructions. We first detect and segment the moving objects using 3D Motion Segmenta-tion approach by exploiting the feature trajectories' behaviours. Getting the segmentations of both the dynamic scene parts and the static scene parts, we propose an efficient point cloud registration approach which takes the advantages of 3-point RANSAC and Iterative Closest Points algorithms to produce precise point cloud alignment. Furthermore, we proposed a point cloud smoothing and texture mapping framework to enhance the results of reconstructions for both the static a…

Computer sciencePoint cloudComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingRANSACPoint Cloud Registration0202 electrical engineering electronic engineering information engineeringSegmentationComputer vision3D Scene Enhancement[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingComputingMethodologies_COMPUTERGRAPHICSMotion Segmentationbusiness.industry3D reconstruction020207 software engineeringFeature (computer vision)Computer Science::Computer Vision and Pattern Recognition020201 artificial intelligence & image processingArtificial intelligencebusiness3D Reconstruction[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingTexture mappingSmoothing
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Extracting cloud motion from satellite image sequences

2004

This paper present a new technique for the estimation of cloud motion, using a sequence of infrared satellite images. It can be considered a challenging task due to the complexity of phenomena implied, as non-linear events and a non-rigid motion. In this circumstances most motion models are not suitable and new algorithms have to be developed. We propose a novel method, combining an Automatic Multilevel Thresholding for image segmentation, a Block Matching Algorithm (BMA) and a best candidate block search along with a vector median regularization.

Computer scienceSegmentation-based object categorizationbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationImage processingPattern recognitionImage segmentationThresholdingImage textureMotion estimationComputer visionArtificial intelligencebusinessBlock-matching algorithm7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002.
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Texture Classification with Generalized Fourier Descriptors in Dimensionality Reduction Context: An Overview Exploration

2008

In the context of texture classification, this article explores the capacity and the performance of some combinations of feature extraction, linear and nonlinear dimensionality reduction techniques and several kinds of classification methods. The performances are evaluated and compared in term of classification error. In order to test our texture classification protocol, the experiment carried out images from two different sources, the well known Brodatz database and our leaf texture images database.

Computer sciencebusiness.industryDimensionality reductionFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONNonlinear dimensionality reductionPattern recognitionContext (language use)Texture (geology)Term (time)symbols.namesakeFourier transformsymbolsArtificial intelligencebusiness
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Image Recognition through Incremental Discriminative Common Vectors

2010

An incremental approach to the discriminative common vector (DCV) method for image recognition is presented. Two different but equivalent ways of computing both common vectors and corresponding subspace projections have been considered in the particular context in which new training data becomes available and learned subspaces may need continuous updating. The two algorithms are based on either scatter matrix eigendecomposition or difference subspace orthonormalization as with the original DCV method. The proposed incremental methods keep the same good properties than the original one but with a dramatic decrease in computational burden when used in this kind of dynamic scenario. Extensive …

Computer sciencebusiness.industryPattern recognitionContext (language use)Machine learningcomputer.software_genreAutomatic image annotationDiscriminative modelImage textureScatter matrixU-matrixComputer visionArtificial intelligencebusinesscomputerSubspace topologyFeature detection (computer vision)
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Magnetic anisotropy in Fe/U and Ni/U bilayers

2021

Magnetometry measurements of Fe/U and Ni/U bilayer systems reveal a non-monotonic dependence of the magnetic anisotropy for U thicknesses in the range 0 nm - 8 nm, with the Fe/U bilayers showing a more prominent effect as compared to Ni/U. The stronger response for Fe/U is ascribed to the stronger 3d-5f hybridization of Fe and U. This non-monotonic behaviour is thought to arise from quantum well states in the uranium overlayers. Estimating an oscillation period from the non-monotonic data, and comparing it to Density Functional Theory calculations, we find that wavevector matches to the experimental data can be made to regions of high spectral density in (010) and (100) cuts of the electron…

Condensed Matter - Materials ScienceMaterials scienceCondensed Matter - Mesoscale and Nanoscale PhysicsCondensed matter physicsMagnetometerOscillationBilayerMaterials Science (cond-mat.mtrl-sci)FOS: Physical sciencesElectronic structurelaw.inventionMagnetic anisotropylawMesoscale and Nanoscale Physics (cond-mat.mes-hall)Wave vectorDensity functional theoryTexture (crystalline)Physical Review B
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Polaronic relaxation in perovskites

1995

We report a low-temperature loss anomaly in several oxidic perovskites such as ${\mathrm{KTaO}}_{3}$, ${\mathrm{KTaO}}_{3}$:Nb, ${\mathrm{SrTiO}}_{3}$, ${\mathrm{SrTiO}}_{3}$:Ca, ${\mathrm{PbTiO}}_{3}$:La, Cu, and ${\mathrm{BaTiO}}_{3}$:La. We show that this anomaly arises from a low-frequency dielectric relaxation. The activation energy and the relaxation time of this process are nearly the same for all the investigated perovskites disregarding their composition, texture, and ferroelectric properties. We thus ascribe the loss anomaly to the localization of polarons on residual defects. Although the dielectric losses in ${\mathrm{SrTiO}}_{3}$ and ${\mathrm{SrTiO}}_{3}$:Ca are qualitatively …

Condensed Matter::Materials ScienceMaterials scienceCondensed matter physicsCondensed Matter::SuperconductivityRelaxation (NMR)Dielectric lossDielectricTexture (crystalline)Activation energyAnomaly (physics)PolaronFerroelectricityPhysical Review B
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