Fabric Defects Detection Using Multi-Scale Wavelet and Locating

2011 ◽  
Vol 331 ◽  
pp. 481-484
Author(s):  
Jun Feng Jing ◽  
Hang Li ◽  
Peng Fei Li
Keyword(s):  
Graphical User Interface ◽  
Extreme Values ◽  
Inspection System ◽  
Zero Crossings ◽  
Multi Scale ◽  
First Derivative ◽  
Detection Approach ◽  
Inflection Points ◽  
Edge Detectors ◽  
Industrial Pc

In this paper, multi-scale wavelet edge detection approach is investigated for real time inspection of diversified fabric texture. Multi-scale edge detectors smooth the signal at various scales and detect sharp variations points from their first or second order derivative. The extreme values of the first derivative correspond to the zero crossings of the second derivative and to the inflection points of the smoothed signal. Quadtree decomposition of segmented defects shows pinpoint location of specific web flaw. Further, preliminary graphical user interface (GUI) was designed so as to facilitate operation. After integrate GUI with procedure, parameters material can be acquired, which is vital to applying the inspection system on industrial PC.

scholarly journals An Multi-scale Edge Detection Approach

2012 ◽  
Vol 25 ◽  
pp. 1616-1620 ◽  
Author(s):  
Chen Zhigang ◽  
Cui Yueli ◽  
Chen Aihua
Keyword(s):  
Edge Detection ◽  
Multi Scale ◽  
Detection Approach

Mathematical models for phase transitions in biogels

2019 ◽  
Vol 33 (09) ◽  
pp. 1950111 ◽  
Author(s):  
Ayse Humeyra Bilge ◽  
Arif Selcuk Ogrenci ◽  
Onder Pekcan
Keyword(s):  
Phase Transitions ◽  
Critical Point ◽  
Transition Point ◽  
Extreme Values ◽  
Sol Gel ◽  
Epidemic Models ◽  
Gel Point ◽  
Sis Model ◽  
First Derivative ◽  
Sir Epidemic

It has been shown that reversible and irreversible phase transitions of biogels can be represented by epidemic models. The irreversible chemical sol–gel transitions are modeled by the Susceptible-Exposed-Infected-Removed (SEIR) or Susceptible-Infected-Removed (SIR) epidemic systems whereas reversible physical gels are modeled by a modification of the Susceptible-Infected-Susceptible (SIS) system. Measured sol–gel and gel–sol transition data have been fitted to the solutions of the epidemic models, either by solving the differential equations directly (SIR and SEIR models) or by nonlinear regression (SIS model). The gel point is represented as the “critical point of sigmoid,” defined as the limit point of the locations of the extreme values of its derivatives. Then, the parameters of the sigmoidal curve representing the gelation process are used to predict the gel point and its relative position with respect to the transition point, that is, the maximum of the first derivative with respect to time. For chemical gels, the gel point is always located before the maximum of the first derivative and moves backward in time as the strength of the activation increases. For physical gels, the critical point for the sol–gel transition occurs before the maximum of the first derivative with respect to time, that is, it is located at the right of this maximum with respect to temperature. For gel–sol transitions, the critical point is close to the transition point; the critical point occurs after the maximum of the first derivative for low concentrations whereas the critical point occurs after the maximum of the first derivative for higher concentrations.

scholarly journals Automatic Road Pavement Assessment with Image Processing: Review and Comparison

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Sylvie Chambon ◽  
Jean-Marc Moliard
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
State Of The Art ◽  
Civil Infrastructures ◽  
The Road ◽  
Multi Scale ◽  
Road Pavement ◽  
Optical Images ◽  
Detection Approach ◽  
Pavement Crack Detection

In the field of noninvasive sensing techniques for civil infrastructures monitoring, this paper addresses the problem of crack detection, in the surface of the French national roads, by automatic analysis of optical images. The first contribution is a state of the art of the image-processing tools applied to civil engineering. The second contribution is about fine-defect detection in pavement surface. The approach is based on a multi-scale extraction and a Markovian segmentation. Third, an evaluation and comparison protocol which has been designed for evaluating this difficult task—the road pavement crack detection—is introduced. Finally, the proposed method is validated, analysed, and compared to a detection approach based on morphological tools.

Multiscale peak detection in wavelet space

The Analyst ◽  
2015 ◽  
Vol 140 (23) ◽  
pp. 7955-7964 ◽  
Author(s):  
Zhi-Min Zhang ◽  
Xia Tong ◽  
Ying Peng ◽  
Pan Ma ◽  
Ming-Jin Zhang ◽  
...  
Keyword(s):  
Full Advantage ◽  
Peak Detection ◽  
Zero Crossings ◽  
Additional Information ◽  
Multi Scale ◽  
Analytical Instruments ◽  
Wavelet Space

Multi-scale peak detection (MSPD) for analytical instruments is presented by taking full advantage of additional information in wavelet space including ridges, valleys, and zero-crossings.

A novel change detection approach for VHR remote sensing images by integrating multi-scale features

2019 ◽  
Vol 40 (13) ◽  
pp. 4910-4933 ◽  
Author(s):  
Ming Hao ◽  
Wenzhong Shi ◽  
Yuanxin Ye ◽  
Hua Zhang ◽  
Kazhong Deng
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Images ◽  
Multi Scale ◽  
Detection Approach

Cointegration and the Empirical Mode Decomposition for the Analysis of Diagnostic Data

2013 ◽  
Vol 569-570 ◽  
pp. 884-891 ◽  
Author(s):  
Ifigeneia Antoniadou ◽  
Elizabeth J. Cross ◽  
Keith Worden
Keyword(s):  
Empirical Mode Decomposition ◽  
Time Scales ◽  
Energy Operator ◽  
Frequency Separation ◽  
Operational Conditions ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Detection Approach ◽  
Empirical Mode Decomposition Method ◽  
The Hilbert Transform

The use of cointegration has been proposed recently as a potentially powerful means of removing confounding influences from structural health monitoring (SHM) data. On the other hand the Empirical Mode Decomposition method is a recent multi-scale decomposition technique with the ability to decompose a signal into meaningful signal components. In this paper the EMD method will be used to highlight the dominant time-scales that have been affected by varying environmental and operational conditions and the time-scales that are related to damage. Then cointegration will be used to remove the nonstationary effects not associated with damage at the time-scales of interest in the data. The final step of the damage detection approach proposed, will be the use of two different amplitude-frequency separation methods, the Hilbert Transform and the more recent Teager Kaiser energy operator approach in order to compare the merits of both, concerning the estimation of the instantaneous characteristics of the signals.

scholarly journals Fast interpolation method for surfaces with faults by multi-scale second-derivative optimization

2020 ◽  
Vol 75 ◽  
pp. 62
Author(s):  
Michel Léger ◽  
Vincent Clochard
Keyword(s):  
Seismic Data ◽  
Single Unit ◽  
Input Data ◽  
Structural Model ◽  
Interpolation Method ◽  
Second Derivative ◽  
Multi Scale ◽  
Surface Interpolation ◽  
First Derivative ◽  
Fault Network

We present a smooth surface interpolation method enabling to take discontinuities (e.g. faults) into account that can be applied to any dataset defined on a regular mesh. We use a second-derivative multi-scale minimization based on a conjugate gradient method. Our multi-scale approach allows the algorithm to process millions of points in a few seconds on a single-unit workstation. The interpolated surface is continuous, as well as its first derivative, except on some lines that have been specified as discontinuities. Application in geosciences are numerous, for instance when a structural model is to be built from points picked on seismic data. The resulting dip of interpolation extends the dip of the input data. The algorithm also works if faults are given by broken lines. We present results from a synthetic and real examples taking into account fault network.

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