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.

• Type: Conference
• Year: 2006
• Where: SIBGRAPI
• Authors: Alex Jesus Cuadros-Vargas, Leandro C. Gerhardinger, Mario de Castro, João Batista Neto, Luis Gustavo Nonato
• Keywords: Computer Graphics, Imesh Image-based Segmentation, Mesh Segmentation Methods, Segmentation Method, SIBGRAPI 2006