Detecting Curvilinear Features Using Structure Tensors

2015 ◽  
Vol 24 (11) ◽  
pp. 3874-3887 ◽  
Author(s):  
Cristian Vicas ◽  
Sergiu Nedevschi
Keyword(s):  
Structure Tensors ◽  
Curvilinear Features

On an Euler-Lagrange equation for color to grayscale conversion

2019 ◽  
Vol 2019 (1) ◽  
pp. 95-98
Author(s):  
Hans Jakob Rivertz
Keyword(s):  
Variational Inequality ◽  
Color Image ◽  
Lagrange Equation ◽  
New Method ◽  
Grayscale Image ◽  
Structure Tensors

In this paper we give a new method to find a grayscale image from a color image. The idea is that the structure tensors of the grayscale image and the color image should be as equal as possible. This is measured by the energy of the tensor differences. We deduce an Euler-Lagrange equation and a second variational inequality. The second variational inequality is remarkably simple in its form. Our equation does not involve several steps, such as finding a gradient first and then integrating it. We show that if a color image is at least two times continuous differentiable, the resulting grayscale image is not necessarily two times continuous differentiable.

scholarly journals A general framework for computing the turbulence structure tensors

2015 ◽  
Vol 106 ◽  
pp. 54-66 ◽  
Author(s):  
F.S. Stylianou ◽  
R. Pecnik ◽  
S.C. Kassinos
Keyword(s):  
General Framework ◽  
Turbulence Structure ◽  
Structure Tensors

Single-point structure tensors in turbulent channel flows with smooth and wavy walls

2019 ◽  
Vol 31 (12) ◽  
pp. 125115 ◽  
Author(s):  
Junlin Yuan ◽  
Aashwin Ananda Mishra ◽  
Giles Brereton ◽  
Gianluca Iaccarino ◽  
Magnus Vartdal
Keyword(s):  
Single Point ◽  
Channel Flows ◽  
Turbulent Channel Flows ◽  
Structure Tensors

On the Modeling of Evolving Elastic and Plastic Anisotropy in the Finite Strain Regime – Application to Deep Drawing Processes

2012 ◽  
Vol 504-506 ◽  
pp. 679-684 ◽  
Author(s):  
Ivaylo N. Vladimirov ◽  
Michael P. Pietryga ◽  
Vivian Tini ◽  
Stefanie Reese
Keyword(s):  
Deep Drawing ◽  
Evolution Equations ◽  
Finite Strain ◽  
Kinematic Hardening ◽  
Plastic Anisotropy ◽  
Material Model ◽  
Internal Variables ◽  
Time Step ◽  
Finite Element Software ◽  
Structure Tensors

In this work, we discuss a finite strain material model for evolving elastic and plastic anisotropy combining nonlinear isotropic and kinematic hardening. The evolution of elastic anisotropy is described by representing the Helmholtz free energy as a function of a family of evolving structure tensors. In addition, plastic anisotropy is modelled via the dependence of the yield surface on the same family of structure tensors. Exploiting the dissipation inequality leads to the interesting result that all tensor-valued internal variables are symmetric. Thus, the integration of the evolution equations can be efficiently performed by means of an algorithm that automatically retains the symmetry of the internal variables in every time step. The material model has been implemented as a user material subroutine UMAT into the commercial finite element software ABAQUS/Standard and has been used for the simulation of the phenomenon of earing during cylindrical deep drawing.

Automatic and semi-automatic extraction of curvilinear features from SAR images

2012 ◽  
Author(s):  
Emre Akyilmaz ◽  
O. Erman Okman ◽  
Fatih Nar ◽  
Müjdat Cetin
Keyword(s):  
Automatic Extraction ◽  
Sar Images ◽  
Curvilinear Features

One-point turbulence structure tensors

2001 ◽  
Vol 428 ◽  
pp. 213-248 ◽  
Author(s):  
S. C. KASSINOS ◽  
W. C. REYNOLDS ◽  
M. M. ROGERS
Keyword(s):  
Turbulence Structure ◽  
Turbulence Statistics ◽  
Local Effects ◽  
Statistical Measures ◽  
Non Local ◽  
The One ◽  
Rate Correlation ◽  
Systematic Framework ◽  
Structure Tensors

The dynamics of the evolution of turbulence statistics depend on the structure of the turbulence. For example, wavenumber anisotropy in homogeneous turbulence is known to affect both the interaction between large and small scales (Kida & Hunt 1989), and the non-local effects of the pressure–strain-rate correlation in the one-point Reynolds stress equations (Reynolds 1989; Cambon et al. 1992). Good quantitative measures of turbulence structure are easy to construct using two-point or spectral data, but one-point measures are needed for the Reynolds-averaged modelling of engineering flows. Here we introduce a systematic framework for exploring the role of turbulence structure in the evolution of one-point turbulence statistics. Five one-point statistical measures of the energy-containing turbulence structure are introduced and used with direct numerical simulations to analyse the role of turbulence structure in several cases of homogeneous and inhomogeneous turbulence undergoing diverse modes of mean deformation. The one-point structure tensors are found to be useful descriptors of turbulence structure, and lead to a deeper understanding of some rather surprising observations from DNS and experiments.

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