期刊名称：International Journal of Computer Technology and Applications
电子版ISSN：2229-6093
出版年度：2013
卷号：4
期号：5
页码：798-804
出版社：Technopark Publications
摘要：This paper identifies the comprising shape priors in image segmentation has become a key problem in computer vision. Most of the researchers were focuses number of existing works and those were limited to a linearized shape space with small distort modes around a mean shape.These approaches are relevant only when the learning set is composed of very similar shapes. Also, there is no guarantee on the visual quality of the resulting shapes. We introduce a non-linear shape prior for the deformable model framework that we learn from a set of shape samples using recent manifold learning techniques.Our method computes a Delaunay triangulation of the reduced space, considered as Euclidean, and uses the resulting space partition to identify the closest neighbors of any given shape based on its Nystrom extension. First, we propose a solution to the pre-image problem and define the projection of a shape onto the manifold. Based on nearest neighbors for the Diffusion distance
