Decomposing Textures Using Exponential Analysis

Citation

Hou Y, Cuyt A, Lee W & Bhowmik D (2021) Decomposing Textures Using Exponential Analysis. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE International Conference on Acoustics, Speech and Signal Processing, Toronto, Ontario, Canada, 06.06.2021-11.06.2021. Piscataway: IEEE. https://doi.org/10.1109/ICASSP39728.2021.9413909

Abstract
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.

Keywords
Exponential analysis; multivariate; image decomposition; texture analysis