Pre-image as Karcher Mean Using Diffusion Maps: Application to Shape and Image Denoising
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Towards segmentation based on a shape prior manifold
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Diffusion maps as a framework for shape modeling
Computer Vision and Image Understanding
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A method is proposed for performing shape analysis of m-surfaces, e.g. planar curves and surfaces, with a geometric interpretation. The analysis uses an implicit surface representation and connects the popular level set approach with shape analysis. The representation is continuous and completely landmark-free. Shapes are represented as points on an infinite-dimensional manifold and the distance between two surfaces is given by the length of a path on this manifold. The analysis is valid in any dimension and examples of applications such as interpolation and clustering are given.