Efficient 3D mesh salient region detection using local homogeneity measure

Visual saliency is defined by the perceptual information that makes possible to detect specific areas which attract to guide the human visual attention. In this study, the authors present an efficient method for salient regions detection on three-dimensional (3D) meshes using weighted graphs representation. To do so, the authors propose a novel 3D surface descriptor based on a local homogeneity measure. Then, they define the similarity measure between vertices using normal deviation similarities, a two-dimensional projection height map, and the mean curvature. The saliency of a vertex is then evaluated as its degree measure based on the local patch descriptor and a height map. In addition, the authors introduce a custom version of hill climbing algorithm in order to segment the 3D mesh regions according to the saliency degree. Furthermore, they show the robustness of their proposed method through different experimental results. Finally, the authors present the stability and robustness of their method with respect to noise.