Constructing a neural system for surface inspection
(2004) 2004 IEEE International Joint Conference on Neural Networks (IJCNN) p.1881-1886- 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/614849
- author
- Grunditz, C. ; Walder, M and Spaanenburg, Lambert LU
- organization
- publishing date
- 2004
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- feature extraction, image mining, image classification, object image detection, visual quality assurance techniques, surface inspection, neural system, protein detection, multiple tier feedforward neural network, metal plate inspection
- host publication
- 2004 IEEE International Joint Conference on Neural Networks
- pages
- 1881 - 1886
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2004 IEEE International Joint Conference on Neural Networks (IJCNN)
- conference location
- Budapest, Hungary
- conference dates
- 2004-07-25 - 2004-07-29
- external identifiers
-
- wos:000224941900326
- scopus:10844252252
- ISBN
- 0-7803-8359-1
- language
- English
- LU publication?
- yes
- id
- 1e6e35db-fa32-42a0-822f-f735ecae2455 (old id 614849)
- alternative location
- http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1380897
- date added to LUP
- 2016-04-04 11:04:54
- date last changed
- 2022-01-29 21:20:09
@inproceedings{1e6e35db-fa32-42a0-822f-f735ecae2455,
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, C. and Walder, M and Spaanenburg, Lambert}},
booktitle = {{2004 IEEE International Joint Conference on Neural Networks}},
isbn = {{0-7803-8359-1}},
keywords = {{feature extraction; image mining; image classification; object image detection; visual quality assurance techniques; surface inspection; neural system; protein detection; multiple tier feedforward neural network; metal plate inspection}},
language = {{eng}},
pages = {{1881--1886}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Constructing a neural system for surface inspection}},
url = {{http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1380897}},
year = {{2004}},
}