Search results for "image features"

showing 3 items of 3 documents

Matching image features.

2011

Settore INF/01 - Informaticaimage features.
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noRANSAC for fundamental matrix estimation

2011

The estimation of the fundamental matrix from a set of corresponding points is a relevant topic in epipolar stereo geometry [10]. Due to the high amount of outliers between the matches, RANSAC-based approaches [7, 13, 29] have been used to obtain the fundamental matrix. In this paper two new contributes are presented: a new normalized epipolar error measure which takes into account the shape of the features used as matches [17] and a new strategy to compare fundamental matrices. The proposed error measure gives good results and it does not depend on the image scale. Moreover, the new evaluation strategy describes a valid tool to compare diffe rent RANSAC-based methods because it does not re…

Evaluation strategyGround truthSettore INF/01 - Informaticabusiness.industryimage features epipolar geometry ransac fundamental matrix estimationEight-point algorithmEpipolar geometryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage scaleRANSACOutlierComputer visionArtificial intelligencebusinessFundamental matrix (computer vision)AlgorithmMathematicsProcedings of the British Machine Vision Conference 2011
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Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models

2012

We propose a computer-aided detection (CAD) system which can detect small-sized (from 3 mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We…

medicine.medical_specialtyLung NeoplasmsDatabases FactualHealth InformaticsCADModels BiologicalSensitivity and SpecificityImaging Three-DimensionalSegmentationLung nodulemedicineFalse positive paradoxSegmentation; Lung nodules; Active contours models;Computer tomography (CT); Mass–spring models; Spline curves; Image featuresHumansSegmentationDiagnosis Computer-AssistedStage (cooking)Lung cancerComputer tomography (CT)business.industryNodule (medicine)Image featuresSpline curvemedicine.diseaseSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Computer Science ApplicationsRegion growingMass–spring modelRadiologyTomographymedicine.symptombusinessTomography Spiral ComputedAlgorithmsActive contours model
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