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
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 t
FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP
Fechado
DOI: https://doi.org/10.1109/SSIAI.2014.6806052
Texto completo: https://ieeexplore.ieee.org/document/6806052
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
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2014 Southwest Symposium on Image Analysis and Interpretation (May, 2014), p. 153-156, n. art. 6806052 |