Quantum implications of a scale invariant regularization

D. M. Ghilencea

doi:10.1103/physrevd.97.075015

Quantum implications of a scale invariant regularization

Physical Review D ◽

10.1103/physrevd.97.075015 ◽

2018 ◽

Vol 97 (7) ◽

Cited By ~ 7

Author(s):

D. M. Ghilencea

Keyword(s):

Scale Invariant ◽

Invariant Regularization

Manifestly scale-invariant regularization and quantum effective operators

Physical Review D ◽

10.1103/physrevd.93.105006 ◽

2016 ◽

Vol 93 (10) ◽

Cited By ~ 22

Author(s):

D. M. Ghilencea

Keyword(s):

Scale Invariant ◽

Invariant Regularization ◽

Effective Operators

A scale-invariant regularization for QED

Physics Letters B ◽

10.1016/0370-2693(79)90615-4 ◽

1979 ◽

Vol 86 (1) ◽

pp. 34-38 ◽

Cited By ~ 1

Author(s):

T. Suzuki

Keyword(s):

Scale Invariant ◽

Invariant Regularization

A manifestly scale-invariant regularization and quantum effective operators

10.22323/1.263.0040 ◽

2016 ◽

Author(s):

Dumitru Ghilencea

Keyword(s):

Scale Invariant ◽

Invariant Regularization ◽

Effective Operators

Hybrid G-PRNU: Optimal parameter selection for scale-invariant asymmetric source smartphone identification

Electronic Imaging ◽

10.2352/issn.2470-1173.2019.5.mwsf-546 ◽

2019 ◽

Vol 2019 (5) ◽

pp. 546-1-546-7 ◽

Cited By ~ 1

Author(s):

Reepjyoti DEKA ◽

Chiara GALDI ◽

Jean-Luc DUGELAY

Keyword(s):

Optimal Parameter ◽

Parameter Selection ◽

Scale Invariant ◽

Selection For ◽

Optimal Parameter Selection

Mean Shift tracking for video moving objects in combination with scale invariant feature transform and Kalman filter

Journal of Computer Applications ◽

10.3724/sp.j.1087.2013.03179 ◽

2013 ◽

Vol 33 (11) ◽

pp. 3179-3182 ◽

Cited By ~ 1

Author(s):

Zhiling ZHU ◽

Qiuqi RUAN

Keyword(s):

Kalman Filter ◽

Moving Objects ◽

Mean Shift ◽

Scale Invariant Feature Transform ◽

Scale Invariant ◽

Invariant Feature ◽

Feature Transform ◽

Scale Invariant Feature

Electronic image stabilization algorithm using improved scale invariant feature transform

Journal of Computer Applications ◽

10.3724/sp.j.1087.2012.02817 ◽

2013 ◽

Vol 32 (10) ◽

pp. 2817-2820

Author(s):

Bo MENG ◽

Guang-liang HAN

Keyword(s):

Scale Invariant Feature Transform ◽

Scale Invariant ◽

Image Stabilization ◽

Invariant Feature ◽

Feature Transform ◽

Scale Invariant Feature

Non-rigid Medical Image Registration Based on Improved Optical Flow Method and Scale-invariant Feature Transform

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2012.01142 ◽

2014 ◽

Vol 35 (5) ◽

pp. 1222-1228 ◽

Cited By ~ 1

Author(s):

Jie-yu Wang ◽

Jia-jun Wang ◽

Jing-ya Zhang

Keyword(s):

Image Registration ◽

Optical Flow ◽

Medical Image ◽

Scale Invariant Feature Transform ◽

Scale Invariant ◽

Flow Method ◽

Optical Flow Method ◽

Invariant Feature ◽

Feature Transform ◽

Scale Invariant Feature

Scale-Invariant Theory of Optical Properties of Fractal Clusters

10.21236/ada227031 ◽

1990 ◽

Author(s):

Vadim A. Markel ◽

Leonid S. Muratov ◽

Mark I. Stockman ◽

Thomas F. George

Keyword(s):

Optical Properties ◽

Invariant Theory ◽

Scale Invariant ◽

Fractal Clusters

A Scale Invariant Clustering using Minimum Volume Ellipsoids

SSRN Electronic Journal ◽

10.2139/ssrn.882115 ◽

2006 ◽

Cited By ~ 2

Author(s):

Mahesh Kumar ◽

James B. Orlin

Keyword(s):

Minimum Volume ◽

Scale Invariant

Best Matching: Technical Details

10.1093/oso/9780198789475.003.0005 ◽

2018 ◽

Cited By ~ 1

Author(s):

Flavio Mercati

Keyword(s):

Point Particle ◽

Euclidean Group ◽

Scale Invariant ◽

Similarity Group ◽

N Body Problem ◽

Technical Details ◽

Matching Procedure ◽

Principal Fibre ◽

Principal Fibre Bundle ◽

Gauge Connection

The best matching procedure described in Chapter 4 is equivalent to the introduction of a principal fibre bundle in configuration space. Essentially one introduces a one-dimensional gauge connection on the time axis, which is a representation of the Euclidean group of rotations and translations (or, possibly, the similarity group which includes dilatations). To accommodate temporal relationalism, the variational principle needs to be invariant under reparametrizations. The simplest way to realize this in point–particle mechanics is to use Jacobi’s reformulation of Mapertuis’ principle. The chapter concludes with the relational reformulation of the Newtonian N-body problem (and its scale-invariant variant).