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Modeling the Marginal Distributions of Complex Wavelet Coefficient Magnitudes for the Classification of Zoom-Endoscopy Images

Roland Kwitt, Andreas Uhl
ICCV 2007 pp. 1-8
doi:10.1109/ICCV.2007.4409170 /iccv/2007/kwitt2007iccv-modeling/

Abstract

In this paper, we propose a set of new image features for the classification of zoom-endoscopy images. The feature extraction step is based on fitting a two-parameter Weibull distribution to the wavelet coefficient magnitudes of sub-bands obtained from a complex wavelet transform variant. We show, that the shape and scale parameter possess more discriminative power than the classic mean and standard deviation based features for complex subband coefficient magnitudes. Furthermore, we discuss why the commonly used Rayleigh distribution model is suboptimal in our case.

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Cite

Text

Kwitt and Uhl. "Modeling the Marginal Distributions of Complex Wavelet Coefficient Magnitudes for the Classification of Zoom-Endoscopy Images." IEEE/CVF International Conference on Computer Vision, 2007. doi:10.1109/ICCV.2007.4409170

Markdown

[Kwitt and Uhl. "Modeling the Marginal Distributions of Complex Wavelet Coefficient Magnitudes for the Classification of Zoom-Endoscopy Images." IEEE/CVF International Conference on Computer Vision, 2007.](https://mlanthology.org/iccv/2007/kwitt2007iccv-modeling/) doi:10.1109/ICCV.2007.4409170

BibTeX

@inproceedings{kwitt2007iccv-modeling,
  title     = {{Modeling the Marginal Distributions of Complex Wavelet Coefficient Magnitudes for the Classification of Zoom-Endoscopy Images}},
  author    = {Kwitt, Roland and Uhl, Andreas},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2007},
  pages     = {1-8},
  doi       = {10.1109/ICCV.2007.4409170},
  url       = {https://mlanthology.org/iccv/2007/kwitt2007iccv-modeling/}
}