Alex Jesus Cuadros-Vargas,  Leandro C. Gerhardinger,  Mario de Castro,  João Batista Neto,  Luis Gustavo Nonato. 

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.

http://doi.ieeecomputersociety.org/10.1109/SIBGRAPI.2006.26

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