Search results for "Image Segmentation"

showing 4 items of 234 documents

Novel VAMPIRE algorithms for quantitative analysis of the retinal vasculature

2013

This paper summarizes three recent, novel algorithms developed within VAMPIRE, namely optic disc and macula detection, arteryvein classification, and enhancement of binary vessel masks, and their performance assessment. VAMPIRE is an international collaboration growing a suite of software tools to allow efficient quantification of morphological properties of the retinal vasculature in large collections of fundus camera images. VAMPIRE measurements are currently mostly used in biomarker research, i.e., investigating associations between the morphology of the retinal vasculature and a number of clinical and cognitive conditions.

retinaRetinaSettore INF/01 - InformaticaContextual image classificationbusiness.industryComputer scienceVampireRetinalImage segmentationClassificationFeature detectionRetina; Feature detection; Segmentation; Classification; Biomarkerschemistry.chemical_compoundSegmentationmedicine.anatomical_structurechemistrymedicineSegmentationComputer visionArtificial intelligencebusinessAlgorithmBiomarkersOptic discFeature detection (computer vision)2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC)
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Interface Detection Using a Quenched-Noise Version of the Edwards-Wilkinson Equation

2015

We report here a multipurpose dynamic-interface-based segmentation tool, suitable for segmenting planar, cylindrical, and spherical surfaces in 3D. The method is fast enough to be used conveniently even for large images. Its implementation is straightforward and can be easily realized in many environments. Its memory consumption is low, and the set of parameters is small and easy to understand. The method is based on the Edwards-Wilkinson equation, which is traditionally used to model the equilibrium fluctuations of a propagating interface under the influence of temporally and spatially varying noise. We report here an adaptation of this equation into multidimensional image segmentation, an…

ta113Image segmentationta114DiscretizationInterface (Java)Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONobject detectionimage edge detectionImage segmentationComputer Graphics and Computer-Aided DesignGrayscaleGray-scaleObject detectionSurface topographyNoiseMathematical modelThree-dimensional displaysSegmentationTomography3D image processingNoiseSurface morphologyAlgorithmSoftwareIEEE Transactions on Image Processing
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A Stochastic Algorithm Based on Fast Marching for Automatic Capacitance Extraction in Non-Manhattan Geometries

2014

WOS:000346854900026 (Nº de Acesso Web of Science) We present an algorithm for two- and three-dimensional capacitance analysis on multidielectric integrated circuits of arbitrary geometry. Our algorithm is stochastic in nature and as such fully parallelizable. It is intended to extract capacitance entries directly from a pixelized representation of the integrated circuit (IC), which can be produced from a scanning electron microscopy image. Preprocessing and monitoring of the capacitance calculation are kept to a minimum, thanks to the use of distance maps automatically generated with a fast marching technique. Numerical validation of the algorithm shows that the systematic error of the algo…

ta113Parallelizable manifoldSEM image segmentationComputer scienceMatemáticasApplied MathematicsGeneral MathematicsFast marchingCapacitance extractionIntegrated circuitResolution (logic)CapacitanceImage (mathematics)law.inventionNon-Manhattan IClawFloating random walkPreprocessorRepresentation (mathematics)AlgorithmFast marching method
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Automatic dynamic texture segmentation using local descriptors and optical flow

2012

A dynamic texture (DT) is an extension of the texture to the temporal domain. How to segment a DT is a challenging problem. In this paper, we address the problem of segmenting a DT into disjoint regions. A DT might be different from its spatial mode (i.e., appearance) and/or temporal mode (i.e., motion field). To this end, we develop a framework based on the appearance and motion modes. For the appearance mode, we use a new local spatial texture descriptor to describe the spatial mode of the DT; for the motion mode, we use the optical flow and the local temporal texture descriptor to represent the temporal variations of the DT. In addition, for the optical flow, we use the histogram of orie…

ta113business.industrySegmentation-based object categorizationComputer scienceTexture DescriptorComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONOptical flowScale-space segmentationPattern recognitionImage segmentationComputer Graphics and Computer-Aided DesignImage textureMotion fieldRegion growingComputer Science::Computer Vision and Pattern RecognitionHistogramComputer visionSegmentationArtificial intelligencebusinessSoftwareIEEE Transactions on Image Processing
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