Deep Structure, Singularities, and Computer Vision
First International Workshop, DSSCV 2005, Maastricht, The Netherlands, June 9-10, 2005, Revised Selected Papers
Chapter and Conference Paper
Based on a diffusion tensor image (DTI) and a tentative tractogram of a fiber bundle we propose a filtering method for operationally defining and removing outliers using . To this end we assign to each track...
Chapter and Conference Paper
We propose a novel connectivity measure between brain regions using diffusion-weighted MRI. This connectivity measure is based on optimal sub-Finslerian geodesic front propagation on the 5D base manifold of (3...
Article
We study 3D-multidirectional images, using Finsler geometry. The application considered here is in medical image analysis, specifically in High Angular Resolution Diffusion Imaging (HARDI) (Tuch et al. in Magn...
Chapter and Conference Paper
We introduce a new method to determine the flow field of an image sequence using multi-scale anchor points. These anchor points manifest themselves in the scale-space representation of an image. The novelty of...
Chapter and Conference Paper
We study a well-known scalar quantity in differential geometry, the Ricci scalar, in the context of Diffusion Tensor Imaging (DTI). We explore the relation between the Ricci scalar and the two most popular sca...
Chapter and Conference Paper
Motion estimation is an important topic in medical image analysis. The investigation and quantification of, e.g., the cardiac movement is important for assessment of cardiac abnormalities and to get an indicat...
Chapter and Conference Paper
Local feature matching is an essential component of many image retrieval algorithms. Euclidean and Mahalanobis distances are mostly used in order to compare two feature vectors. The first distance does not giv...
Chapter and Conference Paper
In this paper we discuss new measures for connectivity analysis of brain white matter, using MR diffusion tensor imaging. Our approach is based on Riemannian geometry, the viability of which has been demonstra...
Chapter and Conference Paper
Scale space interest points capture important photometric and deep structure information of an image. The information content of such points can be made explicit using image reconstruction. In this paper we wi...
Chapter and Conference Paper
A geometric model is proposed for an artificial foveal vision system, and its plausibility in the context of biological vision is explored. The model is based on an isotropic, scale invariant two-form that des...
Chapter and Conference Paper
There is an increasing demand to develop image processing tools for the filtering and analysis of matrix-valued data, so-called matrix fields. In the case of scalar-valued images parabolic partial differential...
Article
Exploration of information content of features that are present in images has led to the development of several reconstruction algorithms. These algorithms aim for a reconstruction from the features that is vi...
Chapter and Conference Paper
In many image analysis applications there is a need to extract curves in noisy images. To achieve a more robust extraction, one can exploit correlations of oriented features over a spatial context in the image...
Chapter and Conference Paper
A new multiscale paradigm is proposed for motion extraction. It exploits the fact that certain geometrically meaningful, isolated points in scale space provide unambiguous motion evidence, and the fact that su...
Article
In this paper we present a novel method to implement the monogenic scale space on a rectangular domain. The monogenic scale space is a vector valued scale space based on the Poisson scale space, which establis...
Book and Conference Proceedings
First International Workshop, DSSCV 2005, Maastricht, The Netherlands, June 9-10, 2005, Revised Selected Papers
Chapter and Conference Paper
Image reconstruction from a fiducial collection of scale space interest points and attributes (e.g. in terms of image derivatives) can be used to make the amount of information contained in them explicit. Prev...
Chapter and Conference Paper
Exploration of information content of features that are present in images has led to the development of several reconstruction algorithms. These algorithms aim for a reconstruction from the features that is vi...
Chapter and Conference Paper
The geometry of “empty” scale space is investigated. By virtue of the proposed geometric axioms the generating PDE, the linear isotropic heat equation, can be presented in covariant, or geometrical form. The p...
Chapter and Conference Paper
We compare the topology and deep structure of alternative scale space representations, so called α-scale spaces, 1/2 ≤ α ≤ 1, which are subject to a first order pseudo partial differential equation on the upper h...