Linear flaw detection in woven textiles using model-based clustering

J. G. Campbell, C. Fraley, F. Murtagh, A. E. Raftery

Research output: Contribution to journalArticle

101 Citations (Scopus)

Abstract

We combine image-processing techniques with a powerful new statistical technique to detect linear pattern production faults in woven textiles. Our approach detects a linear pattern in preprocessed images via model-based clustering. It employs an approximate Bayes factor which provides a criterion for assessing the evidence for the presence of a defect. The model used in experimentation is a (possibly highly elliptical) Gaussian cloud superimposed on Poisson clutter. Results are shown for some representative examples, and contrasted with a Hough transform. Software for the statistical modeling is available.

Original languageEnglish
Pages (from-to)1539-1548
Number of pages10
JournalPattern Recognition Letters
Volume18
Issue number14
DOIs
Publication statusPublished - Dec 1997
Externally publishedYes

Fingerprint Dive into the research topics of 'Linear flaw detection in woven textiles using model-based clustering'. Together they form a unique fingerprint.

  • Cite this