scholarly journals Recognition of Different Fracture Surface Morphologies using Computer Image Processing Technique

1993 ◽  
Vol 36 (2) ◽  
pp. 220-227 ◽  
Author(s):  
Kenjiro Komai ◽  
Kohji Minoshima ◽  
Shoich Ishii
Keyword(s):  
Image Processing ◽  
Fracture Surface ◽  
Computer Image ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computer Image Processing ◽  
Surface Morphologies

scholarly journals Visualization of Supersonic Flow Using Computer Image Processing Technique.

1999 ◽  
Vol 19 (73) ◽  
pp. 141-146
Author(s):  
Masayuki HASHIMOTO ◽  
Takaaki SHIZAWA ◽  
Shinji HONAMI ◽  
Akira MURAKAMI ◽  
Shigemi SHINDO ◽  
...  
Keyword(s):  
Image Processing ◽  
Supersonic Flow ◽  
Computer Image ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computer Image Processing

scholarly journals Identify Toxin Contamination in Peanuts Using the Development of Machine Vision Based on Image Processing Technique

2016 ◽  
Vol 3 (3) ◽  
pp. 83
Author(s):  
Atris Suyantohadi ◽  
Rudiati Evi Masithoh
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Vision System ◽  
Region Of Interest ◽  
Processing Technique ◽  
Mean Value ◽  
Image Processing Technique ◽  
Computer Image Processing ◽  
Uv Lamp ◽  
Object Area

<p>This research aimed at identifying the use of image processing technique and classifying the use of K-mean clustering of contaminated and uncontaminated peanuts. A machine vision system was made of a small aluminum box, equipped with a camera, a petri for placing the sample, USB connector, UV lamp, and a computer. Image processing methods consisted of analysis of the average color of RGB in the region of interest, convertion of RGB into HSV, segmentation process, as well as convertion image into grayscale and binary objects in order to obtain the total number of pixels value in the object area so that the mean value of the pixels of the area can be calculated. K-means algorithm was used to classify the contaminated and uncontaminated peanuts based on the average pixel value of R,G, B color parameters. The accuracy of a system was 100% meaning that the performance of machine vision can be used to identify the contaminated and uncontaminated peanuts.</p><p><strong>Keywords</strong>: aflatoxin, K-mean clustering, machine vision, image processing, peanuts </p>

An Application of Computer Image Processing Technique to Decoration Pattern Design

2014 ◽  
Vol 998-999 ◽  
pp. 1009-1012
Author(s):  
Xu Guang Yang ◽  
Qi Hui Lin
Keyword(s):  
Image Processing ◽  
Computer Aided Design ◽  
Rapid Development ◽  
Processing Technique ◽  
Digital Data ◽  
Image Processing Technique ◽  
Pattern Design ◽  
New Era ◽  
Computer Image Processing ◽  
Aided Design

The rapid development and image processing technique to the computer is updated day by day, which lead the decorating pattern design to extend into the computer aided design from the original manual drawing.Decorative pattern is more huge miscellaneous from the contents with changeful graphics form, In this kind of advanced tool, with the help of computer of decorative pattern from figure element content changes to the graphics can carry on a processing and save in the form of digital data, can also make use of the computer to carry on the conventional image to it what processing technique falls through processing,thus form decorate pattern special manifestation and characteristics of the new era.

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