Detecting Shape of Weld Defect Image on X-ray Film by Image Processing Applied Genetic Algorithm
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- AOKI Kimiya
- Department of Information and Computer Sciences, Toyohashi University of Technology
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- SUGA Yasuo
- Faculty of Science and Technology, Department of Mechanical Engineering, Keio University
Bibliographic Information
- Other Title
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- Detecting Shape of Weld Defect Image on X-ray Film by lmage Processing Applied Genetic Algorithm
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Abstract
Several types of non-destructive testing methods are used for detecting weld defects. Because the X-ray radiographic testing method is particularly useful in inspecting the inside of a weld metal, it is often used in industry. However, since the number of skilled inspectors for X-ray radiographic testing has been gradually decreasing, recently, several methods to detect weld defects from films automatically have been investigated to improve the quality of the detection results. However, X-ray film images contain much noise, and defect images show very low contrast and various shapes in spite of the same kind of defect. Moreover, boundaries between a defect image and the background are unclear, making it difficult to automate the inspection of X-ray films. If the type of defect image were to be judged by an expert system or a neural network which learns the rules of professional inspectors, the boundaries of the defect image would have to be detected in a manner similar to recognition by a human's (or an inspector's) sense of vision. Therefore, in this study, a new image processing method applied genetic algorithms that were a method of optimization, was constructed and applied to the detection of defect boundaries in detail.
Journal
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- JSME International Journal Series C
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JSME International Journal Series C 45 (2), 534-542, 2002
The Japan Society of Mechanical Engineers
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Details
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- CRID
- 1390282679655916544
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- NII Article ID
- 110004225643
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- NII Book ID
- AA11179487
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- ISSN
- 1347538X
- 13447653
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- NDL BIB ID
- 6190098
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- Text Lang
- en
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- Data Source
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- JaLC
- IRDB
- NDL
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- CiNii Articles
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- Abstract License Flag
- Disallowed