Restructured Eigenfilter Matching for Novelty Detection in Random Textures

A Monadjemi, M Mirmehdi, B Thomas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

A new eigenfilter-based novelty detection approach to find abnormalities in random textures is presented. The proposed algorithm reconstructs a given texture twice using a subset of its own eigenfilter bank and a subset of a reference (template) eigenfilter bank, and measures the reconstruction error as the level of novelty. We then present an improved reconstruction generated by structurally matched eigenfilters through rotation, negation, and mirroring. We apply the method to the detection of defects in textured ceramic tiles. The method is over $90\%$ accurate, and is fast and amenable to implementation on a production line.
Translated title of the contributionRestructured Eigenfilter Matching for Novelty Detection in Random Textures
Original languageEnglish
Title of host publicationUnknown
PublisherBMVA Press
Pages637 - 646
Number of pages9
ISBN (Print)1901725251
Publication statusPublished - Sept 2004

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the 15th British Machine Vision Conference

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