{"title":"Direct Matrix Factorization and Alignment Refinement: Application to Defect Detection","authors":"Zhen Qin, P. Beek, Xu Chen","doi":"10.1109/CRV.2014.26","DOIUrl":null,"url":null,"abstract":"Defect detection approaches based on template differencing require precise alignment of the input and template image, however, such alignment is easily affected by the presence of defects. Often, non-trivial pre/post-processing steps and/or manual parameter tuning are needed to remove false alarms, complicating the system and hampering automation. In this work, we explicitly address alignment and defect extraction jointly, and provide a general iterative algorithm to improve both their performance to pixel-wise accuracy. We achieve this by utilizing and extending the robust rank minimization and alignment method of [12]. We propose an effective and efficient optimization algorithm to decompose a template-guided image matrix into a low-rank part relating to alignment-refined defect-free images and an explicit error component containing the defects of interest. Our algorithm is fully automatic, training-free, only needs trivial pre/post-processing procedures, and has few parameters. The rank minimization formulation only requires a linearly correlated template image, and a template-guided approach relieves the common assumption of small defects, making our system very general. We demonstrate the performance of our novel approach qualitatively and quantitatively on a real-world data-set with defects of varying appearance.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Canadian Conference on Computer and Robot Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CRV.2014.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Defect detection approaches based on template differencing require precise alignment of the input and template image, however, such alignment is easily affected by the presence of defects. Often, non-trivial pre/post-processing steps and/or manual parameter tuning are needed to remove false alarms, complicating the system and hampering automation. In this work, we explicitly address alignment and defect extraction jointly, and provide a general iterative algorithm to improve both their performance to pixel-wise accuracy. We achieve this by utilizing and extending the robust rank minimization and alignment method of [12]. We propose an effective and efficient optimization algorithm to decompose a template-guided image matrix into a low-rank part relating to alignment-refined defect-free images and an explicit error component containing the defects of interest. Our algorithm is fully automatic, training-free, only needs trivial pre/post-processing procedures, and has few parameters. The rank minimization formulation only requires a linearly correlated template image, and a template-guided approach relieves the common assumption of small defects, making our system very general. We demonstrate the performance of our novel approach qualitatively and quantitatively on a real-world data-set with defects of varying appearance.