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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
Department光电信息技术研究室
Conference Name9th International Conference on Graphic and Image Processing, ICGIP 2017
Conference DateOctober 14-16, 2017
Conference PlaceQingdao, China
Author of SourceOcean University of China ; University of Portsmouth
Source PublicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Publication PlaceBellingham, WA
2017
Pages1-9
Indexed ByEI ; CPCI(ISTP)
EI Accession number20181905168354
WOS IDWOS:000434707200067
Contribution Rank1
ISSN0277-786X
ISBN978-15106-1741-4
Abstract

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.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22062
Collection光电信息技术研究室
Corresponding AuthorWang XJ(王学娟)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, China;
2.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
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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