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Image segmentation through combined methods : watershed transform, unsupervised distance learning and normalized cut

Image segmentation through combined methods : watershed transform, unsupervised distance learning and normalized cut

Tiago W. Pinto, Marco A. G. de Carvalho, Daniel C. G. Pedronette, Paulo S. Martins

ARTIGO

Inglês

Agradecimentos: FAPESP grant 2013/08645-0

Este artigo foi apresentado no evento Southwest Symposium on Image Analysis and Interpretation (SSIAI), 2014

Abstract: Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some... Ver mais

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP

2013/08645-0

Fechado

Image segmentation through combined methods : watershed transform, unsupervised distance learning and normalized cut

Tiago W. Pinto, Marco A. G. de Carvalho, Daniel C. G. Pedronette, Paulo S. Martins

										

Image segmentation through combined methods : watershed transform, unsupervised distance learning and normalized cut

Tiago W. Pinto, Marco A. G. de Carvalho, Daniel C. G. Pedronette, Paulo S. Martins

    Fontes

    Proceedings of the 2014 Southwest Symposium on Image Analysis and Interpretation - Fonte avulsa)

    Piscataway, NJ : Institute of Electrical and Electronics Engineers, 2014.

    p. 153-156