GIRAUD, Rémi; TA, Vinh-Thong; BUGEAU, Aurélie; COUPÉ, Pierrick; PAPADAKIS, Nicolas

doi:10.1109/TIP.2017.2708504

Metadatos

Licencia de uso del documento

GIRAUD, Rémi
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut de Mathématiques de Bordeaux [IMB]

TA, Vinh-Thong
Institut Polytechnique de Bordeaux [Bordeaux INP]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

BUGEAU, Aurélie
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

Idioma

Article de revue

Este ítem está publicado en

IEEE Transactions on Image Processing. 2017

Institute of Electrical and Electronics Engineers

Resumen en inglés

Superpixels have become very popular in many computer vision applications. Nevertheless, they remain underexploited since the superpixel decomposition may produce irregular and non stable segmentation results due to the dependency to the image content. In this paper, we first introduce a novel structure, a superpixel-based patch, called SuperPatch. The proposed structure, based on superpixel neighborhood, leads to a robust descriptor since spatial information is naturally included. The generalization of the PatchMatch method to SuperPatches, named SuperPatchMatch, is introduced. Finally, we propose a framework to perform fast segmentation and labeling from an image database, and demonstrate the potential of our approach since we outperform, in terms of computational cost and accuracy, the results of state-of-the-art methods on both face labeling and medical image segmentation.< Leer menos

Palabras clave en inglés

Patch-based method

PatchMatch

Labeling

Superpixels

Segmentation

URI

https://oskar-bordeaux.fr/handle/20.500.12278/193655

DOI

http://dx.doi.org/10.1109/TIP.2017.2708504

Proyecto ANR

Generalized Optimal Transport Models for Image processing - ANR-16-CE33-0010

Orígen

Importado de Hal

Centros de investigación

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251