{"title":"A system for detection of internal log defects by computer analysis of axial CT images","authors":"S. Bhandarkar, T. D. Faust, Mengjin Tang","doi":"10.1109/ACV.1996.572064","DOIUrl":null,"url":null,"abstract":"The paper presents a system for detection of some important internal log defects via analysis of axial CT images. Two major procedures are used. The first is the segmentation of a single computer tomography (CT) image slice which extracts defect-like regions from the image slice, the second is correlation analysis of the defect-like regions across CT image slices. The segmentation algorithm for a single CT image is basically a complex form of multiple thresholding that exploits both the prior knowledge of wood structure and gray value characteristics of the image. The defect-like region extraction algorithm first locates the pith, groups the pixels in the segmented image on the basis of their connectivity and classifies each region as either a defect-like region or a defect-free region using shape, orientation and morphological features. Each defect-like region is classified as a defect or non-defect via correlation analysis across corresponding defect-like regions in neighboring CT image slices.","PeriodicalId":222106,"journal":{"name":"Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACV.1996.572064","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 26
Abstract
The paper presents a system for detection of some important internal log defects via analysis of axial CT images. Two major procedures are used. The first is the segmentation of a single computer tomography (CT) image slice which extracts defect-like regions from the image slice, the second is correlation analysis of the defect-like regions across CT image slices. The segmentation algorithm for a single CT image is basically a complex form of multiple thresholding that exploits both the prior knowledge of wood structure and gray value characteristics of the image. The defect-like region extraction algorithm first locates the pith, groups the pixels in the segmented image on the basis of their connectivity and classifies each region as either a defect-like region or a defect-free region using shape, orientation and morphological features. Each defect-like region is classified as a defect or non-defect via correlation analysis across corresponding defect-like regions in neighboring CT image slices.