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According to Marr's paradigm of computational vision the first process is an extraction of relevant features. The goal of this paper is to quantify and characterize the information carried by features using image-structure measured at feature-points to reconstruct images. In this way, we indirectly evaluate the concept of feature-based image analysis. The(More)
Segmentation of anatomical structures in medical images is often based on a voxel/pixel classification approach. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images that fosters categorization. We propose a novel system for voxel classification integrating three 2D CNNs, which have a(More)
Manifolds are widely used to model non-linearity arising in a range of computer vision applications. This paper treats statistics on manifolds and the loss of accuracy occurring when linearizing the mani-fold prior to performing statistical operations. Using recent advances in manifold computations, we present a comparison between the non-linear analog of(More)
The importance of manifolds and Riemannian geometry is spreading to applied fields in which the need to model non-linear structure has spurred widespread interest in geometry. The transfer of interest has created demand for methods for computing classical constructs of geometry on manifolds occurring in practical applications. This paper develops initial(More)
The purpose of this report 1 is to deene optic ow for scalar and density images without using a priori knowledge other than its deening conservation principle, and to incorporate measurement duality, notably the scale-space paradigm. It is argued that the design of optic ow based applications may beneet from a manifest separation between factual image(More)
In order to develop statistical methods for shapes with a tree-structure, we construct a shape space framework for treelike shapes and study metrics on the shape space. The shape space has singularities, which correspond to topological transitions in the represented trees. We study two closely related metrics, TED and QED. The QED is a quotient euclidean(More)