Utilize este identificador para referenciar este registo: http://hdl.handle.net/1822/17658

TítuloTexture classification of images from endoscopic capsule by using MLP and SVM – a comparative approach
AutorLima, C. S.
Correia, J. H.
Ramos, J.
Babosa, Daniel
Palavras-chaveCapsule endoscopy
Texture analysis
Discrete wavelet transform
Multilayer perceptrons
Support vector machines
Data12-Set-2009
EditoraSpringer
ResumoThis 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.
TipoconferenceObject
URIhttp://hdl.handle.net/1822/17658
Versão da editorahttp://science.icmcc.org/2009/09/07/world-congress-on-medical-physics-and-biomedical-engineering/
Arbitragem científicayes
AcessoopenAccess
Aparece nas coleções:DEI - Artigos em atas de congressos internacionais

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