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dc.contributor.authorLima, C. S.-
dc.contributor.authorCorreia, J. H.-
dc.contributor.authorRamos, J.-
dc.contributor.authorBabosa, Daniel-
dc.description.abstractThis article reports a comparative study of Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) in the classification of endoscopic capsule images. Texture information is coded by second order statistics of color image levels extracted from co-occurrence matrices. The co-occurrence matrices are computed from images rich in texture information. These images are obtained by processing the original images in the wavelet domain in order to select the most important information concerning texture description. Texture descriptors calculated from co-occurrence matrices are then modeled by using third and forth order moments in order to cope with non-Gaussianity, which appears especially in some pathological cases. Several color spaces are used, namely the most simple RGB, the most related to the human perception HSV, and the one that best separates light and color information, which uses luminance and color differences, usually known as YCbCr.por
dc.description.sponsorshipCentre Algoritmipor
dc.publisherSpringer por
dc.subjectCapsule endoscopypor
dc.subjectTexture analysispor
dc.subjectDiscrete wavelet transformpor
dc.subjectMultilayer perceptronspor
dc.subjectSupport vector machinespor
dc.subjectMultilayer Perceptrons and Support Vector Machinespor
dc.titleTexture classification of images from endoscopic capsule by using MLP and SVM – a comparative approachpor
oaire.citationConferenceDate07 - 12 september 2009por
oaire.citationConferencePlaceMunich, Germanypor
oaire.citationTitleThe World Congress on Medical Physics and Biomedical Engineering 2009por
dc.subject.wosScience & Technologypor
sdum.journalIfmbe Proceedingspor
sdum.conferencePublicationThe World Congress on Medical Physics and Biomedical Engineering 2009por
Appears in Collections:DEI - Artigos em atas de congressos internacionais

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