| An artificial immune approach for optical image based vision inspection |
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| 作者姓名： | 郑宏 肖南风 蓝金辉 |
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| 作者单位： | School of Electronic Information，Wuhan University，Wuhan 430079，School of Computer Science & Engineering，South China University of Technology Guangzhou 510641，Department of Precision Instruments，Tsinghua University，Beijng 100084 |
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| 基金项目： | This project was partially supported by the National Natural Science Foundation under grant No. 40271094.H. |
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| 摘 要： | This paper presents a novel approach of visual inspection for texture surface defects. The approach uses artificial immune theory in learning the detection of texture defects. In this paper, texture defects are regards as non-self, and normal textures are regarded as self. Defect filters and segmentation thresholds used for defect detection are regarded as antibodies. The clonal selection algorithm stemmed from the natural immune system is employed to learn antibodies. Experimental results on textile image inspection are presented to illustrate the merit and feasibility of the proposed method.
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An artificial immune approach for optical image based vision inspection |
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| Abstract: | This paper presents a novel approach of visual inspection for texture surface defects. The approach usesartificial immune theory in learning the detection of texture defects. In this paper, texture defects areregards as non-self, and normal textures are regarded as self. Defect filters and segmentation thresholdsused for defect detection are regarded as antibodies. The clonal selection algorithm stemmed from thenatural immune system is employed to learn antibodies. Experimental results on textile image inspectionare presented to illustrate the merit and feasibility of the proposed method. |
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