A parametrisable system for physically plausible surface inspection
(2004) In Journal of Intelligent & Fuzzy Systems 15(1). p.29-39- Abstract
- Visual quality assurance techniques focus on the detection and qualification of abnormal structures in the image of an object. The features of abnormality are extracted through image mining, whereupon classification is performed on characteristic combinations. Many techniques for feature extraction have been proposed, but the feed-forward neural network is seldom utilized despite its popularity in other application areas. Based on the wide experience base, this paper shows how a multi-tier feed-forward network can be constructed to model detectable peaks using only the physical properties of the image domain. This generic architecture can easily be adapted for different applications, as in metal plate inspection and protein detection, with... (More)
- Visual quality assurance techniques focus on the detection and qualification of abnormal structures in the image of an object. The features of abnormality are extracted through image mining, whereupon classification is performed on characteristic combinations. Many techniques for feature extraction have been proposed, but the feed-forward neural network is seldom utilized despite its popularity in other application areas. Based on the wide experience base, this paper shows how a multi-tier feed-forward network can be constructed to model detectable peaks using only the physical properties of the image domain. This generic architecture can easily be adapted for different applications, as in metal plate inspection and protein detection, with mean error rate below 5%. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/254344
- author
- Grunditz, CH and Spaanenburg, Lambert LU
- organization
- publishing date
- 2004
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Intelligent & Fuzzy Systems
- volume
- 15
- issue
- 1
- pages
- 29 - 39
- publisher
- IOS Press
- external identifiers
-
- wos:000226874700005
- scopus:10044265439
- ISSN
- 1064-1246
- language
- English
- LU publication?
- yes
- id
- 65232bfc-a18c-478c-b8e1-6f7a514ea6ef (old id 254344)
- date added to LUP
- 2016-04-01 17:02:42
- date last changed
- 2022-01-28 23:55:40
@article{65232bfc-a18c-478c-b8e1-6f7a514ea6ef,
abstract = {{Visual quality assurance techniques focus on the detection and qualification of abnormal structures in the image of an object. The features of abnormality are extracted through image mining, whereupon classification is performed on characteristic combinations. Many techniques for feature extraction have been proposed, but the feed-forward neural network is seldom utilized despite its popularity in other application areas. Based on the wide experience base, this paper shows how a multi-tier feed-forward network can be constructed to model detectable peaks using only the physical properties of the image domain. This generic architecture can easily be adapted for different applications, as in metal plate inspection and protein detection, with mean error rate below 5%.}},
author = {{Grunditz, CH and Spaanenburg, Lambert}},
issn = {{1064-1246}},
language = {{eng}},
number = {{1}},
pages = {{29--39}},
publisher = {{IOS Press}},
series = {{Journal of Intelligent & Fuzzy Systems}},
title = {{A parametrisable system for physically plausible surface inspection}},
volume = {{15}},
year = {{2004}},
}