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Volumn 213, Issue 2, 1999, Pages 127-133

Synergistic classification systems for wood defect identification

Author keywords

Automated visual inspection; Classification; Synergy; Wood inspection

Indexed keywords

WOOD;

EID: 0032859959     PISSN: 09544089     EISSN: None     Source Type: Journal    
DOI: 10.1243/0954408991529825     Document Type: Article
Times cited : (5)

References (18)
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  • 5
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  • 6
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    • Pham, D.T.1    Bayro-Corrochano, E.J.2
  • 9
    • 0031071044 scopus 로고    scopus 로고
    • Machine vision using artificial neural networks with local 3D neighborhoods
    • Schmoldt, D. L.,Li, P.and Abbott, A. L. Machine vision using artificial neural networks with local 3D neighborhoods. Comput. Electronics Agriculture, 1997, 16(3), 255-271.
    • (1997) Comput. Electronics Agriculture , vol.16 , Issue.3 , pp. 255-271
    • Schmoldt, D.L.1    Li, P.2    Abbott, A.L.3
  • 13
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    • A neural network approach to machine vision systems for automated industrial inspection
    • Seattle, Washington, USA
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    • Cho, T.H.1    Conners, R.W.2
  • 16
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    • Selection of features for wood defect classification by a multi-layer perceptron
    • Intelligent Systems Laboratory, School of Engineering, University of Wales Cardiff
    • Pham, D. T.and Alcock, R. J. Selection of features for wood defect classification by a multi-layer perceptron. Technical report, Intelligent Systems Laboratory, School of Engineering, University of Wales Cardiff.
    • Technical Report
    • Pham, D.T.1    Alcock, R.J.2
  • 17
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    • An efficient algorithm for automatic knowledge acquisition
    • Pham, D. T.and Dimov, S. S. An efficient algorithm for automatic knowledge acquisition. Pattern Recognition, 1997,30(7), 1137-1143.
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    • Pham, D.T.1    Dimov, S.S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.