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Texture Decomposition by Harmonics Extraction From Higher Order Statistics
Abstract
In this paper, a method of harmonics extraction from Higher Order Statistics (HOS) is developed for texture decomposition. We show that the diagonal slice of the fourth-order cumulants is proportional to the autocorrelation of a related noiseless sinusoidal signal with identical frequencies. We propose to use this fourth-order cumulants slice to estimate a power spectrum from which the harmonic frequencies can be easily extracted. Hence, a texture can be decomposed into deterministic components and indeterministic components as in a unified texture model through a Wold-like decomposition procedure. The simulation and experimental results demonstrated that this method is effective for texture decomposition and it performs better than traditional lower order statistics based decomposition methods.
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Cited by
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Joan, S. P. F., & Valli, S. (2017) · Information Systems Frontiers · doi:10.1007/s10796-016-9699-x · Cited by 17
Metadata
- Authors
- Huang, Y., & Chan, K. L.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2004-01
- Volume
- 13
- Issue
- 1
- Pages
- 1-14
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2109315325
- Cited by
- 180
- References
- 0
- FWCI
- 4.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- Huang, Y., & Chan, K. L. (2004). Texture Decomposition by Harmonics Extraction From Higher Order Statistics. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2003.819432
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