Abstract. 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. We model a c...
In this paper we present a novel class-based segmentation method, which is guided by a stored representation of the shape of objects within a general class (such as horse images). ...
Most of the traditional methods for shape classification are based on contour. They often encounter difficulties when dealing with classes that have large nonlinear variability, es...
Xingwei Yang, Xiang Bai, Deguang Yu, Longin Jan La...
We present an image-based approach to infer 3D structure parameters using a probabilistic "shape+structure" model. The 3D shape of an object class is represented by sets...
Kristen Grauman, Gregory Shakhnarovich, Trevor Dar...
Abstract. In this paper we propose a new variational framework for image segmentation that incorporates the information of expected shape and a few points on the boundary into geod...
Yunmei Chen, Weihong Guo, Feng Huang, David Cliffo...