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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)
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)
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 mean is often the most important statistic of a dataset as it provides a single point that summarizes the entire set. While the mean is readily defined and computed in Euclidean spaces, no commonly accepted solutions are currently available in more complicated spaces, such as spaces of tree-structured data. In this paper we study the notion of means,(More)
To develop statistical methods for shapes with a tree-structure, we construct a shape space framework for tree-shapes and study metrics on the shape space. This shape space has singularities which correspond to topological transitions in the represented trees. We study two closely related metrics on the shape space, TED and QED. QED is a quotient euclidean(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)
Computational vision often needs to deal with derivatives of digital images. Such derivatives are not intrinsic properties of digital data; a paradigm is required to make them well-deened. Normally, a linear ltering is applied. This can be formulated in terms of scale-space, functional minimization, or edge detection lters. The main emphasis of this paper(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)