Constructing a neural system for surface inspection
(2004) Joint SAIS/SSLS Workshop, 2004 p.68-73- 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 this 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,... (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 this 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/1028969
- author
- Grunditz, Carl-Henrik ; Walder, Martin and Spaanenburg, Lambert LU
- organization
- publishing date
- 2004
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- SAIS Workshop
- editor
- Malec, Jacek
- pages
- 68 - 73
- publisher
- SAIS
- conference name
- Joint SAIS/SSLS Workshop, 2004
- conference location
- Lund, Sweden
- conference dates
- 2004-04-15 - 2004-04-16
- external identifiers
-
- scopus:10844252252
- language
- English
- LU publication?
- yes
- id
- 1297a587-f844-4da1-b21f-035953ebc573 (old id 1028969)
- date added to LUP
- 2016-04-04 11:42:03
- date last changed
- 2022-01-29 22:20:16
@inproceedings{1297a587-f844-4da1-b21f-035953ebc573,
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 this 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, Carl-Henrik and Walder, Martin and Spaanenburg, Lambert}},
booktitle = {{SAIS Workshop}},
editor = {{Malec, Jacek}},
language = {{eng}},
pages = {{68--73}},
publisher = {{SAIS}},
title = {{Constructing a neural system for surface inspection}},
year = {{2004}},
}