We study the problem of projecting high-dimensional tensor data on an unspecified Riemannian manifold onto some lower dimensional subspace1 without much distorting the pairwise geo...
The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that capture a single event from a large number of vantage po...
Mark A. Davenport, Chinmay Hegde, Marco F. Duarte,...
We study nonparametric regression between Riemannian manifolds based on regularized empirical risk minimization. Regularization functionals for mappings between manifolds should re...
The concept of weak stability boundary has been successfully used in the design of several fuel efficient space missions. In this paper we give a rigorous definition of the weak st...
Abstract. In set-based face recognition, each set of face images is often represented as a linear/nonlinear manifold and the Principal Angles (PA) or Kernel PAs are exploited to me...
In this paper, we revisit the manifold assumption which has been widely adopted in the learning-based image superresolution. The assumption states that point-pairs from the high-r...
Postprocedural analysis of gastrointestinal (GI) endoscopic videos is a difficult task because the videos often suffer from a large number of poor-quality frames due to the motion...
Selen Atasoy, Diana Mateus, Joe Lallemand, Alexand...
We propose a framework for the extraction of biomarkers from low-dimensional manifolds representing inter- and intra-subject brain variation in MR image data. The coordinates of ea...
Robin Wolz, Paul Aljabar, Joseph V. Hajnal, Daniel...
Abstract— We present a proof for the probabilistic completeness of RRT-based algorithms when planning with constraints on end-effector pose. Pose constraints can induce lowerdime...
A straightforward way to illustrate a 3D model is to use a line drawing. Faces in a 2D line drawing provide important information for reconstructing its 3D geometry. Manifold objec...