Aplications of Image Processing Techniques on the Structural Characterization of Decagonal Phases

1990 ◽  
Vol 183 ◽  
Author(s):  
J. Reyes-Gasga ◽  
R. Perez ◽  
M. Jose-Yacaman
Keyword(s):  
Image Processing ◽  
Structural Characterization ◽  
Density Wave ◽  
Image Processing Techniques ◽  
Decagonal Phase ◽  
Processing Techniques ◽  
Simulated Images ◽  
Stm Images

AbstractImage processing techniques applied to HREM images of decagonal quasicrystalline phases are carried out. A comparison between these processed images, simulated images based on the multislice method and density wave techniques and also experimental STM images of the decagonal phase [9] suggest some insights on their structure.

Characterization of Air-Entrainment in a Plunging Water Jet System Using Image Processing: An Educational Approach

2011 ◽  
Author(s):  
Arman Molki ◽  
Lyes Khezzar ◽  
Afshin Goharzadeh
Keyword(s):  
Image Processing ◽  
Free Surface ◽  
Flow Behavior ◽  
Air Entrainment ◽  
Experimental Setup ◽  
Water Mixture ◽  
Educational Approach ◽  
Image Processing Techniques ◽  
Processing Techniques

This paper outlines a proposed experimental setup and image processing techniques using MATLAB for the characterization of the average dynamic behavior of the air/water mixture under the free surface of water penetrated by a plunging jet. The proposed setup focuses on the dynamics of air entrainment below the free surface and the identification of the major regimes related to the entrainment process of bubbles in water, namely: (a) no-entrainment, (b) incipient entrainment, (c) intermittent entrainment, and (d) continuous entrainment. The experimental setup allows students to observe the flow behavior below the free liquid surface and determine the penetration depth of the bubble plumes using image processing techniques in MATLAB. The focal point of the experiment is image analysis for qualitative and quantitative characterization of the bubble plume.

Characterization of blood samples using image processing techniques

2011 ◽  
Vol 172 (1) ◽  
pp. 308-314 ◽  
Author(s):  
Ana Ferraz ◽  
Vitor Carvalho ◽  
Filomena Soares ◽  
Celina P. Leão
Keyword(s):  
Image Processing ◽  
Blood Samples ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals Study of avalanche dynamics by seismic methods, image-processing techniques and numerical models

1998 ◽  
Vol 26 ◽  
pp. 319-323 ◽  
Author(s):  
F. Sabot ◽  
M. Naaim ◽  
F. Granada ◽  
E. Suriñach ◽  
P. Planet ◽  
...  
Keyword(s):  
Image Processing ◽  
Seismic Data ◽  
Numerical Models ◽  
Maximum Velocity ◽  
Seismic Signals ◽  
Image Processing Techniques ◽  
Video Images ◽  
Avalanche Flow ◽  
Processing Techniques

Seismic signals of avalanches, related video images and numerical models were compared to improve the characterization of avalanche phenomena. Seismic data and video images from two artificially released avalanches were analysed to obtain more information about the origin of the signals. Image processing was used to compare the evolution of one avalanche front and the corresponding seismic signals. A numerical model was also used to simulate an avalanche flow in order to obtain mean- and maximum-velocity profiles. Prior to this, the simulated avalanche was verified using video images. The results indicate that the seismic signals recorded correspond to changes in avalanche type and path slope, interaction with obstacles and to phenomena associated with the stopping stage of the avalanche, suggesting that only part of the avalanche was recorded. These results account for the seismic signals previously obtained automatically in a wide avalanche area.

Microstructure Characterization of Ceramics by Fractal Geometry

1997 ◽  
Vol 492 ◽  
Author(s):  
Z. X. Xiong ◽  
K. Z. Baba-Kishi ◽  
F. G. Shin
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Fractal Dimension ◽  
Fractal Geometry ◽  
Fractal Theory ◽  
Image Processing Techniques ◽  
Fractal Analyses ◽  
Processing Techniques ◽  
Good Agreement

ABSTRACTFollowing Mandelbrot's fractal theory, the irregular characteristics of the microstructural features of ferroelectric Pb(Sc0.5 Ta0.5)O3 ceramics, including grain boundaries and dislocation networks, were investigated. The microstructural features were imaged by electron microscopy. The fractal analyses were carried out manually and by image processing techniques, which show the value of the fractal dimension, D, varies according to the regularity of the microstructure. The value of, D, close to unity is an indication of an increasing degree of microstructural regularity, which is in good agreement with the simulated results.

scholarly journals Characterization of cracking in pavement distress using image processing techniques and k-Nearest neighbour

2019 ◽  
Vol 14 (2) ◽  
pp. 810 ◽  
Author(s):  
A. Ibrahim ◽  
M.K. Osman ◽  
N.A.M. Yusof ◽  
K.A. Ahmad ◽  
N.H. Harun ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Acquisition ◽  
Automated Image Analysis ◽  
Image Analysis System ◽  
Nearest Neighbour ◽  
Pavement Distress ◽  
Image Processing Techniques ◽  
Knn Classifier ◽  
Processing Techniques

<p class="Abstract">This study presents characterization of cracking in pavement distress using image processing techniques and <em>k</em>-nearest neighbour (kNN) classifier. The proposed semi-automated detection system for characterization on pavement distress anticipated to minimize the human supervision from traditional surveys and reduces cost of maintenance of pavement distress. The system consists of 4 stages which are image acquisition, image processing, feature extraction and classification. Firstly, a tool for image acquisition, consisting of digital camera, camera holder and tripod, is developed to capture images of pavement distress. Secondly, image processing techniques such as image thresholding, median filter, image erosion and image filling are applied. Thirdly, two features that represent the length of pavement cracking in <em>x</em> and <em>y</em> coordinate system namely <em>delta_x</em> and <em>delta_y</em> are computed. Finally, the computed features is fed to a kNN classifier to build its committee and further used to classify the pavement cracking into two types; transverse and longitudinal cracking. The performance of kNN classifier in classifying the type of pavement cracking is also compared with a modified version of kNN called fuzzy kNN classifier. Based on the results from images analysis, the semi-automated image processing system is able to consistently characterize the crack pattern with accuracy up to 90%. The comparison of analysed data with field data shows good agreement in the pavement distress characterization. Thus the encouraging results of semi-automated image analysis system will be useful for developing a more efficient road maintenance process.</p>

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