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SIBGRAPI
2006
IEEE

Improving 2D mesh image segmentation with Markovian Random Fields

10 years 8 months ago
Improving 2D mesh image segmentation with Markovian Random Fields
Traditional mesh segmentation methods normally operate on geometrical models with no image information. On the other hand, 2D image-based mesh generation and segmentation counterparts, such as Imesh [6] perform the task by following a set of well defined rules derived from the geometry of the triangles, but with no statistical information of the mesh elements. This paper presents a novel segmentation method that combines the original Imesh image-based segmentation approach with Markovian Random Field (MRF) models. It takes an image as input, generate a mesh of triangles and, by treating the mesh as a Markovian field, produces quality unsupervised segmentation. The results have demonstrated that the method not only provides better segmentation than that of original Imesh, but is also capable of producing MRF-like segmentation output for certain types of images, with considerable cut in processing times.
Alex Jesus Cuadros-Vargas, Leandro C. Gerhardinger
Added 12 Jun 2010
Updated 12 Jun 2010
Type Conference
Year 2006
Where SIBGRAPI
Authors Alex Jesus Cuadros-Vargas, Leandro C. Gerhardinger, Mario de Castro, João Batista Neto, Luis Gustavo Nonato
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