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Detecting wood surface defects with fusion algorithm of visual saliency and local threshold segmentation
Wang XJ(王学娟)1,2; Wu SH(邬抒航)1,2; Liu YP(刘云鹏)1,2
作者部门光电信息技术研究室
会议名称9th International Conference on Graphic and Image Processing, ICGIP 2017
会议日期October 14-16, 2017
会议地点Qingdao, China
会议主办者Ocean University of China ; University of Portsmouth
会议录名称Proceedings of SPIE - The International Society for Optical Engineering
出版者SPIE
出版地Bellingham, WA
2017
页码1-9
收录类别EI ; CPCI(ISTP)
EI收录号20181905168354
WOS记录号WOS:000434707200067
产权排序1
ISSN号0277-786X
ISBN号978-15106-1741-4
摘要

This paper presents a new method for wood defect detection. It can solve the over-segmentation problem existing in local threshold segmentation methods. This method effectively takes advantages of visual saliency and local threshold segmentation. Firstly, defect areas are coarsely located by using spectral residual method to calculate global visual saliency of them. Then, the threshold segmentation of maximum inter-class variance method is adopted for positioning and segmenting the wood surface defects precisely around the coarse located areas. Lastly, we use mathematical morphology to process the binary images after segmentation, which reduces the noise and small false objects. Experiments on test images of insect hole, dead knot and sound knot show that the method we proposed obtains ideal segmentation results and is superior to the existing segmentation methods based on edge detection, OSTU and threshold segmentation.

语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/22062
专题光电信息技术研究室
通讯作者Wang XJ(王学娟)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, China;
2.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
推荐引用方式
GB/T 7714
Wang XJ,Wu SH,Liu YP. Detecting wood surface defects with fusion algorithm of visual saliency and local threshold segmentation[C]//Ocean University of China, University of Portsmouth. Bellingham, WA:SPIE,2017:1-9.
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