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Constructing a neur...
Constructing a neural system for surface inspection
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Grunditz, C. (författare)
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Walder, M (författare)
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- Spaanenburg, Lambert (författare)
- Lund University,Lunds universitet,Institutionen för elektro- och informationsteknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Electrical and Information Technology,Departments at LTH,Faculty of Engineering, LTH
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Malec, Jacek (redaktör/utgivare)
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(creator_code:org_t)
- 2004
- 2004
- Engelska.
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Ingår i: 2004 IEEE International Joint Conference on Neural Networks. - 0780383591 ; , s. 1881-1886, s. 68-73
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Abstract
Ämnesord
Stäng
- 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%
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- 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
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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