Moment Invariants Based on Two Dimensional Non-separable Wavelet Transform

LIU Bin; GAO Qiang

doi:10.11999/JEIT151218

Volume 38 Issue 8

Sep. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(8): 2085-2090

LIU Bin, GAO Qiang. Moment Invariants Based on Two Dimensional Non-separable Wavelet Transform[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2085-2090. doi: 10.11999/JEIT151218

Citation:

LIU Bin, GAO Qiang. Moment Invariants Based on Two Dimensional Non-separable Wavelet Transform[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2085-2090. doi: 10.11999/JEIT151218

LIU Bin, GAO Qiang. Moment Invariants Based on Two Dimensional Non-separable Wavelet Transform[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2085-2090. doi: 10.11999/JEIT151218

Citation:

LIU Bin, GAO Qiang. Moment Invariants Based on Two Dimensional Non-separable Wavelet Transform[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2085-2090. doi: 10.11999/JEIT151218

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Moment Invariants Based on Two Dimensional Non-separable Wavelet Transform

doi: 10.11999/JEIT151218

LIU Bin¹,
GAO Qiang¹

Funds:

The National Natural Science Foundation of China (61471160), The Key Project of the Natural Science of Hubei Province (2012FFA053)

Received Date: 2015-11-03
Rev Recd Date: 2016-05-03
Publish Date: 2016-08-19

Abstract

Abstract

Searching for wavelet invariants is a key issue in multiresolution analysis. On the other hand,the method of moment invariants is fully developed both in the theory and the practice. A kind of wavelet moment invariants are given based on the image invariant moments and wavelet appr-oximation coefficients from the limited number of scales of the image. A fairy complete result on theory and experiment is obtained. At the same time, some problems of the theory and method are pointed out in the practical application.Finally, the application relationship between multi-scale analysis and invariant moment is briefly described.
- Pattern recognition,
- Multi-scale analysis,
- Two dimensional non-separable wavelet,
- Invariant moment,
- Smoothness,
- Approximation coefficients

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References(28)

References

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