Results 191 to 200 of about 3,252 (223)

A Method for Human Pose Estimation and Joint Angle Computation Through Deep Learning. [PDF]

J Imaging
Ciardiello L, Agnello P, Petyx M, Martinelli F, Cesarelli M, Santone A, Mercaldo F.   +6 more
europepmc   +1 more source

Splitting unramified Brauer classes by abelian torsors and the period-index problem. [PDF]

Math Ann
Huybrechts D, Mattei D.
europepmc   +1 more source

Pursuing the double affine Grassmannian II: Convolution

Advances in Mathematics, 2012
This is the second paper of a series (started by Braverman and Finkelberg, 2010 [2]) which describes a conjectural analog of the affine Grassmannian for affine Kac–Moody groups (also known as the double affine Grassmannian).
Alexander Braverman, Michael Finkelberg   +1 more
exaly   +2 more sources

Grassmannian frames with applications to coding and communication

Applied and Computational Harmonic Analysis, 2003
For a given class F of unit norm frames of fixed redundancy we define a Grassmannian frame as one that minimizes the maximal correlation |〈fk,fl〉| among all frames {fk}k∈I∈F. We first analyze finite-dimensional Grassmannian frames.
Thomas Strohmer, Robert W Heath
exaly   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

On Lagrangian–Grassmannian codes

Designs, Codes, and Cryptography, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Felipe Zaldivar
exaly   +2 more sources

Grassmannian sparse representations

Journal of Electronic Imaging, 2015
We present Grassmannian sparse representations (GSR), a sparse representation Grassmann learning framework for efficient classification. Sparse representation classification offers a powerful approach for recognition in a variety of contexts. However, a major drawback of sparse representation methods is their computational performance and memory ...
Sherif Azary, Andreas E. Savakis
openaire   +1 more source

Regression uncertainty on the Grassmannian

2017
Trends in longitudinal or cross-sectional studies over time are often captured through regression models. In their simplest manifestation, these regression models are formulated in ℝn. However, in the context of imaging studies, the objects of interest which are to be regressed are frequently best modeled as elements of a Riemannian manifold ...
Yi Hong 0006, Xiao Yang 0002, Roland Kwitt, Martin Styner, Marc Niethammer   +4 more
openaire   +2 more sources

Caps Embedded in Grassmannians

Geometriae Dedicata, 1998
A \(k\)-cap in a finite projective space \(\pi\) is a set of \(k\) points, no three of which are collinear. For \(\pi=\)PG\((5,q)\), \(q\) a power of \(2\), the authors construct a cap \(C\) of size \(q^3+2q^2+1\) which is maximally embedded in a Klein quadric \(K\) of \(\pi\); this means that \(C\) is contained in \(K\) and \(C\) cannot be extended to
Ebert, G. L., Metsch, K., Szönyi, T.
openaire   +2 more sources