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Decomposing textures using exponential analysis

Lookup NU author(s): Dr Deepayan BhowmikORCiD


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© 2021 IEEE. Decomposition is integral to most image processing algorithms and often required in texture analysis. We present a new approach using a recent 2-dimensional exponential analysis technique. Exponential analysis offers the advantage of sparsity in the model and continuity in the parameters. This results in a much more compact representation of textures when compared to traditional Fourier or wavelet transform techniques. Our experiments include synthetic as well as real texture images from standard benchmark datasets. The results outperform FFT in representing texture patterns with significantly fewer terms while retaining RMSE values after reconstruction. The underlying periodic complex exponential model works best for texture patterns that are homogeneous. We demonstrate the usefulness of the method in two common vision processing application examples, namely texture classification and defect detection.

Publication metadata

Author(s): Hou Y, Cuyt A, Lee W-S, Bhowmik D

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021)

Year of Conference: 2021

Pages: 1920-1924

Online publication date: 13/05/2021

Acceptance date: 02/04/2018

ISSN: 2379-190X

Publisher: IEEE


DOI: 10.1109/ICASSP39728.2021.9413909

Library holdings: Search Newcastle University Library for this item

ISBN: 9781728176055