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Volumn 2016-November, Issue , 2016, Pages 2799-2802

Using deep neural networks for synthetic aperture radar image registration

Author keywords

deep learning; deep neural network; image registration; RANSAC; SAR

Indexed keywords


EID: 85007451874     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2016.7729723     Document Type: Conference Paper
Times cited : (28)

References (10)
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  • 2
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  • 4
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    • ImageNet classification with deep convolutional neural networks
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    • (2012) Proc. Adv. Neural Inf. Process. Syst. , pp. 1097-1105
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 5
    • 0019574599 scopus 로고
    • Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
    • M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, " Communications of the ACM, 24 (6), pp. 381-395, 1981.
    • (1981) Communications of the ACM , vol.24 , Issue.6 , pp. 381-395
    • Fischler, M.A.1    Bolles, R.C.2
  • 7
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  • 9
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  • 10
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    • An FFT-based technique for translation, rotation, and scale-invariant image registration
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    • (1996) IEEE Transactions on Image Processing , vol.5 , Issue.8 , pp. 1266-1271
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.