@article{oai:repo.lib.tut.ac.jp:00001154,
 author = {Aoki, Kimiya and Suga, Yasuo and AOKI, Kimiya and SUGA, Yasuo},
 issue = {2},
 journal = {JSME international journal. Series C, Mechanical systems, machine elements and manufacturing, JSME international journal. Series C, Mechanical systems, machine elements and manufacturing},
 month = {Jun},
 note = {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., ・rights：日本機械学会
・rights：本文データは学協会の許諾に基づきCiNiiから複製したものである
・relation：isVersionOf：http://ci.nii.ac.jp/naid/110004225643/},
 pages = {534--542},
 title = {Detecting Shape of Weld Defect Image on X-ray Film by Image Processing Applied Genetic Algorithm},
 volume = {45},
 year = {2002}
}