Slant Plane CSAR Processing Using Householder Transform

J. Burki; C.F. Barnes

doi:10.1109/tip.2008.2002161

Slant Plane CSAR Processing Using Householder Transform

IEEE Transactions on Image Processing ◽

10.1109/tip.2008.2002161 ◽

2008 ◽

Vol 17 (10) ◽

pp. 1900-1907 ◽

Cited By ~ 10

Author(s):

J. Burki ◽

C.F. Barnes

Keyword(s):

Householder Transform

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Parser adaptation via Householder transform

2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100) ◽

10.1109/icassp.2000.859187 ◽

2002 ◽

Author(s):

Xiaoqiang Luo

Keyword(s):

Householder Transform

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On the Householder Transform in Cm

Digital Signal Processing ◽

10.1006/dspr.1995.1010 ◽

1995 ◽

Vol 5 (2) ◽

pp. 116-117 ◽

Cited By ~ 3

Author(s):

X.G. Xia ◽

B.W. Suter

Keyword(s):

Householder Transform

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Applying Modified Householder Transform to Kalman Filter

2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID) ◽

10.1109/vlsid.2019.00092 ◽

2019 ◽

Cited By ~ 1

Author(s):

Farhad Merchant ◽

Tarun Vatwani ◽

Anupam Chattopadhyay ◽

Soumyendu Raha ◽

S K Nandy ◽

...

Keyword(s):

Kalman Filter ◽

Householder Transform

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On the Design of an On-line Complex Householder Transform

2006 Fortieth Asilomar Conference on Signals, Systems and Computers ◽

10.1109/acssc.2006.354759 ◽

2006 ◽

Cited By ~ 2

Author(s):

Robert McIlhenny ◽

Milos D. Ercegovac

Keyword(s):

Line Complex ◽

On Line ◽

Householder Transform

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Based on Householder transform of the Zernike polynomial wavefront fitting method to solve active optics correction force

10.1117/12.760340 ◽

2007 ◽

Cited By ~ 1

Author(s):

Fu-guo Wang ◽

Fei Yang ◽

Xiao-xia Wu ◽

Hong-wei Liu

Keyword(s):

Active Optics ◽

Zernike Polynomial ◽

Fitting Method ◽

Householder Transform

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A spectral estimation algorithm using the householder transform

Journal of Electronics (China) ◽

10.1007/bf02784416 ◽

1991 ◽

Vol 8 (1) ◽

pp. 77-85

Author(s):

Huili Yu

Keyword(s):

Spectral Estimation ◽

Estimation Algorithm ◽

Householder Transform

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Efficient Realization of Householder Transform Through Algorithm-Architecture Co-Design for Acceleration of QR Factorization

IEEE Transactions on Parallel and Distributed Systems ◽

10.1109/tpds.2018.2803820 ◽

2018 ◽

Vol 29 (8) ◽

pp. 1707-1720 ◽

Cited By ~ 4

Author(s):

Farhad Merchant ◽

Tarun Vatwani ◽

Anupam Chattopadhyay ◽

Soumyendu Raha ◽

S. K. Nandy ◽

...

Keyword(s):

Qr Factorization ◽

Householder Transform ◽

Efficient Realization

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The complex Householder transform

IEEE Transactions on Signal Processing ◽

10.1109/78.622959 ◽

1997 ◽

Vol 45 (9) ◽

pp. 2374-2376 ◽

Cited By ~ 41

Author(s):

Kuo-Liang Chung ◽

Wen-Ming Yan

Keyword(s):

Householder Transform

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Householder Transform in Cm

Digital Signal Processing ◽

10.1006/dspr.1993.1029 ◽

1993 ◽

Vol 3 (4) ◽

pp. 226-227 ◽

Cited By ~ 6

Author(s):

V.Ch. Venkaiah ◽

V.V. Krishna ◽

A. Paulraj

Keyword(s):

Householder Transform

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Robust Watermarking Using n-Diagonalization Based on Householder Transform

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2014.p0549 ◽

2014 ◽

Vol 18 (4) ◽

pp. 549-557

Author(s):

Jaesung Park ◽

◽

Kazuhito Sawase ◽

Hajime Nobuhara

Keyword(s):

Image Watermarking ◽

Singular Values ◽

Discrete Wavelet ◽

Robust Watermarking ◽

Orthogonal Matrices ◽

Information Quantity ◽

Control Information ◽

Value Decomposition ◽

Householder Transform ◽

Better Than

Digital image watermarking based on singular value decomposition (SVD) is highly robust against misuse, but lacks the ability to distinguish whether watermarks are correct due to the importance of singular values being lower than two orthogonal matrices. To achieve highly accurate watermark extraction while maintaining high robustness, we propose robust watermarking based on discrete wavelet transform (DWT) and n-diagonalization formalized by Householder transformation. We propose that DWT be used to ensure visibility and that n-diagonalization be used to control information quantity related to watermark extraction accuracy. Experimental results confirm the robustness of our proposed method and that the extraction accuracy of the proposed method is approximately 2 times better than that of SVD.

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