MDS-Based Segmentation Model For The Fusion of Contour and Texture Cues in Natural Images

Max Mignotte
DIRO, Département d'Informatique et de Recherche Opérationnelle, CP 6128,
Succ. Centre-Ville, P.O. 6128, Montréal (Québec), H3C 3J7.


ABSTRACT

In this work, we propose an original segmentation model based on a preliminary spatially adaptive non-linear dimensionality data reduction step integrating contour and texture cues. This new dimensionality reduction model aims at converting an input texture image into a noisy color image in order to greatly simplify its segmentation. In this de-texturing model, the spatially adaptive non-local constraints based on edge and contour cues allows us to efficiently regularize the resulting de-textured color image and to efficiently combine region and edge cues in a data fusion model used as data pre-processing step in a segmentation model.


SEGMENTATION RESULTS