Search results for "Segmentation"

showing 10 items of 674 documents

Toward a virtual reconstruction of an antique three-dimensional marble puzzle

2017

International audience; Abstract | Introduction | Related Work | Acquisition Setup, Proposed Prototype: Calibration and Visibility | Preprocessing of Scanned Three-Dimensional Fragment Data | Processing of Scanned Three-Dimensional Surface Data: Matching | Conclusion and Future Works | Appendices | Acknowledgments | ReferencesAbstract. The reconstruction of broken objects is an important field of research for many applications, such as art restoration, surgery, forensics, and solving puzzles. In archaeology, the reconstruction of broken artifacts is a very time-consuming task due to the handling of fractured objects, which are generally fragile. However, it can now be supported by three-dim…

[ INFO ] Computer Science [cs]Computer scienceAntique[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]02 engineering and technology[SDV.MHEP.CHI]Life Sciences [q-bio]/Human health and pathology/SurgeryField (computer science)Task (project management)Domain (software engineering)Data acquisitionComputer graphics (images)Clouds[ INFO.INFO-TI ] Computer Science [cs]/Image ProcessingVirtual reconstruction0202 electrical engineering electronic engineering information engineering[INFO.INFO-IM]Computer Science [cs]/Medical ImagingComputer visionScanning[INFO]Computer Science [cs][ SDV.IB ] Life Sciences [q-bio]/BioengineeringComputing systems[ SDV.MHEP.CHI ] Life Sciences [q-bio]/Human health and pathology/SurgeryElectrical and Electronic EngineeringScanners[ INFO.INFO-DS ] Computer Science [cs]/Data Structures and Algorithms [cs.DS]Image segmentation[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industryLasers020207 software engineeringImage segmentation3D modelingCamerasAtomic and Molecular Physics and OpticsComputer Science Applications[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Calibration020201 artificial intelligence & image processingSurgery[SDV.IB]Life Sciences [q-bio]/BioengineeringArtificial intelligencebusinessAlgorithms
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An Image Segmentation Algorithm based on Community Detection

2016

International audience; With the recent advances in complex networks, image segmentation becomes one of the most appropriate application areas. In this context, we propose in this paper a new perspective of image segmentation by applying two efficient community detection algorithms. By considering regions as communities, these methods can give an over-segmented image that has many small regions. So, the proposed algorithms are improved to automatically merge those neighboring regions agglomerative to achieve the highest modularity/stability. To produce sizable regions and detect homogeneous communities, we use the combination of a feature based on the Histogram of Oriented Gradients of the …

[ INFO ] Computer Science [cs]Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentation02 engineering and technology[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Minimum spanning tree-based segmentationImage texture0202 electrical engineering electronic engineering information engineeringcommunity detection[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]Segmentation[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]modularityImage segmentationSegmentation-based object categorizationbusiness.industry[ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO]Pattern recognitionImage segmentationcomplex networksHistogram of oriented gradientsRegion growing020201 artificial intelligence & image processingArtificial intelligencebusiness
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Cardiac motion tracking using a deformable 2D-mesh modeling

2001

International audience; Abstract: The work reported here deals with movement tracking in sequences of medical images in order to quantify the general movements and deformations of the heart For this purpose, we partition the first image into triangular patches in order that each object of the image corresponds to a set of triangles. Then, the nodes of the mesh are tracked across the image sequence giving a mesh which warps with the images. The method is applied to cardiac image sequences where the study of the deformation of the triangles is applied to the determination of the movement of the ventricles

[ INFO.INFO-IM ] Computer Science [cs]/Medical ImagingMovement (music)business.industryComputer sciencePhysics::Medical Physics[INFO.INFO-IM] Computer Science [cs]/Medical ImagingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologyImage segmentationDeformation (meteorology)Tracking (particle physics)030218 nuclear medicine & medical imagingImage (mathematics)Set (abstract data type)03 medical and health sciences0302 clinical medicineComputer Science::Computer Vision and Pattern Recognition0202 electrical engineering electronic engineering information engineeringMedical imaging[INFO.INFO-IM]Computer Science [cs]/Medical Imaging020201 artificial intelligence & image processingComputer visionArtificial intelligencebusinessComputingMethodologies_COMPUTERGRAPHICS
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A Supervised Learning Framework for Automatic Prostate Segmentation in Trans Rectal Ultrasound Images

2012

International audience; Heterogeneous intensity distribution inside the prostate gland, significant variations in prostate shape, size, inter dataset contrast variations, and imaging artifacts like shadow regions and speckle in Trans Rectal Ultrasound (TRUS) images challenge computer aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose a supervised learning schema based on random forest for automatic initialization and propagation of statistical shape and appearance model. Parametric representation of the statistical model of shape and appearance is derived from principal component analysis (PCA) of the probability distribution inside the prostate and PC…

[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industryComputer sciencePosterior probabilitySupervised learning[INFO.INFO-IM] Computer Science [cs]/Medical ImagingStatistical modelPattern recognition02 engineering and technology030218 nuclear medicine & medical imagingRandom forestActive appearance model03 medical and health sciences0302 clinical medicinePoint distribution model0202 electrical engineering electronic engineering information engineering[INFO.INFO-IM]Computer Science [cs]/Medical Imaging020201 artificial intelligence & image processingComputer visionSegmentationArtificial intelligencebusinessParametric statistics
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Multiple Mean Models of Statistical Shape and Probability Priors for Automatic Prostate Segmentation

2011

International audience; Low contrast of the prostate gland, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow regions, speckle and significant variations in prostate shape, size and in- ter dataset contrast in Trans Rectal Ultrasound (TRUS) images challenge computer aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose a probabilistic framework for automatic initialization and propagation of multiple mean parametric models derived from principal component analysis of shape and posterior probability information of the prostate region to segment the prostate. Unlike traditional statistical models of shape and int…

[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industryPosterior probability[INFO.INFO-IM] Computer Science [cs]/Medical ImagingProbabilistic logicInitializationStatistical modelPattern recognition02 engineering and technology030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicinePrior probabilityParametric modelPrincipal component analysis[INFO.INFO-IM]Computer Science [cs]/Medical Imaging0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingSegmentationArtificial intelligencebusinessMathematics
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Analysis of Low-Altitude Aerial Sequences for Road Traffic Diagnosis using Graph Partitioning and Markov Hierarchical Models

2016

International audience; This article focuses on an original approach aiming the processing of low-altitude aerial sequences taken from an helicopter (or drone) and presenting a road traffic. Proposed system attempts to extract vehicles from acquired sequences. Our approach begins with detecting the primitives of sequence images. At the time of this step of segmentation, the system computes dominant motion for each pair of images. This motion is computed using wavelets analysis on optical flow equation and robust techniques. Interesting areas (areas not affected by the dominant motion) are detected thanks to a Markov hierarchical model. Primitives stemming from segmentation and interesting a…

[ INFO.INFO-MO ] Computer Science [cs]/Modeling and SimulationComputer scienceOptical flowTraffic-MonitoringHierarchical database model[ SPI.GCIV.IT ] Engineering Sciences [physics]/Civil Engineering/Infrastructures de transport[SPI.GCIV.IT]Engineering Sciences [physics]/Civil Engineering/Infrastructures de transportWavelet0502 economics and businessSegmentationComputer vision050210 logistics & transportationImage segmentationMarkov chainPerceptual Organizationbusiness.industry05 social sciencesGraph partition[SPI.GCIV.IT] Engineering Sciences [physics]/Civil Engineering/Infrastructures de transportPattern recognitionImage segmentationScene Analysis[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation[SPI.TRON] Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/ElectronicsGraph PartitioningGraph (abstract data type)Artificial intelligenceMarkov Hierarchical Models[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulationbusiness
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A Constrained Band Selection Method Based on Information Measures for Spectral Image Color Visualization

2011

International audience; We present a new method for the visualization of spectral images, based on a selection of three relevant spectral channels to build a Red-Green-Blue composite. Band selection is achieved by means of information measures at the first, second and third orders. Irrelevant channels are preliminarily removed by means of a center-surround entropy comparison. A visualization-oriented spectrum segmentation based on the use of color matching functions allows for computational ease and adjustment of the natural rendering. Results from the proposed method are presented and objectively compared to four other dimensionality reduction techniques in terms of naturalness and informa…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing010504 meteorology & atmospheric sciencesBand SelectionComputer science0211 other engineering and technologies02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciencesRendering (computer graphics)Spectrum SegmentationData visualization[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingColor Matching FunctionsEntropy (information theory)Computer visionSegmentationElectrical and Electronic Engineering021101 geological & geomatics engineering0105 earth and related environmental sciencesVisualizationInformation Measuresbusiness.industryDimensionality reductionPattern recognitionImage segmentationVisualizationMulti/hyperspectral imageryGeneral Earth and Planetary SciencesArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Needle-shape quality control by shadowgraphic image processing

2011

International audience; We propose a needle-shape quality-control method. To this end, we have devised a new acquisition system that combines a camera and a backlight. Needle measurements are carried out at a micrometric scale using shadowgraphic image processing. Our method not only distinguishes good needles from bad ones, but also allows classifying flawed needles into various categories of defects. This classification is important because some categories of defects can affect the entire production, whereas others do not. The results of our needle-shape quality-control method are validated using real samples directly off the manufacturing line. Needles are correctly classified at >97%, a…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer scienceImage qualityImage processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyBacklightMathematical morphology[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingneedle020204 information systems[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringComputer visionquality controlImage sensorRadon transformbusiness.industryGeneral EngineeringImage segmentationAtomic and Molecular Physics and Opticsimage processingmetrology[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingShape analysis (digital geometry)Optical Engineering
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Spatially variant dimensionality reduction for the visualization of multi/hyperspectral images

2011

International audience; In this paper, we introduce a new approach for color visu- alization of multi/hyperspectral images. Unlike traditional methods, we propose to operate a local analysis instead of considering that all the pixels are part of the same population. It takes a segmentation map as an input and then achieves a dimensionality reduction adaptively inside each class of pixels. Moreover, in order to avoid unappealing discon- tinuities between regions, we propose to make use of a set of distance transform maps to weigh the mapping applied to each pixel with regard to its relative location with classes' centroids. Results on two hyperspec- tral datasets illustrate the efficiency of…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer sciencePopulation0211 other engineering and technologiesComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineeringComputer visionSegmentationspectral imageseducationspatially variantvisualization021101 geological & geomatics engineeringdimensionality reductioneducation.field_of_studyPixelbusiness.industryDimensionality reductionHyperspectral imagingIndependent component analysisVisualizationComputer Science::Computer Vision and Pattern Recognition020201 artificial intelligence & image processingArtificial intelligencebusinessDistance transform[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Region-based segmentation on depth images from a 3D reference surface for tree species recognition.

2013

International audience; The aim of the work presented in this paper is to develop a method for the automatic identification of tree species using Terrestrial Light Detection and Ranging (T-LiDAR) data. The approach that we propose analyses depth images built from 3D point clouds corresponding to a 30 cm segment of the tree trunk in order to extract characteristic shape features used for classifying the different tree species using the Random Forest classifier. We will present the method used to transform the 3D point cloud to a depth image and the region based segmentation method used to segment the depth images before shape features are computed on the segmented images. Our approach has be…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer science[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingFeature extractionPoint cloudComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentation[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Minimum spanning tree-based segmentation[STAT.AP] Statistics [stat]/Applications [stat.AP][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringSegmentationComputer vision[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[STAT.AP]Statistics [stat]/Applications [stat.AP]Contextual image classificationbusiness.industry[ STAT.AP ] Statistics [stat]/Applications [stat.AP][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineeringPattern recognitionImage segmentation15. Life on landdepth image segmentationRandom forestdepth images from 3D point cloudsIEEE[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]020201 artificial intelligence & image processingsingle tree species recognitionArtificial intelligenceRange segmentationbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingForest inventory
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