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Related Experiment Video

Updated: Jul 7, 2026

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Published on: October 21, 2018

Wavelet-based rotational invariant roughness features for texture classification and segmentation.

Dimitrios Charalampidis¹, Takis Kasparis

¹Dept. of Electr. Eng., New Orleans Univ., LA 70148, USA. dcharala@uno.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 5, 2008

Summary

This summary is machine-generated.

This study introduces a new rotational invariant feature set for image texture analysis. This method enhances texture segmentation and classification accuracy by combining single- and multiple-scale roughness features.

Related Experiment Videos

Last Updated: Jul 7, 2026

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Published on: October 21, 2018

Area of Science:

Computer Vision
Image Processing
Pattern Recognition

Background:

Traditional fractal dimension (FD) features capture image roughness across all scales.
Scale-dependent texture properties require analysis at individual scales.
Existing methods may not fully capture complex textural characteristics.

Purpose of the Study:

To develop a rotational invariant feature set for improved texture segmentation and classification.
To extend fractal dimension (FD) features by incorporating single- and multiple-scale analysis.
To create a robust feature set for characterizing scale-dependent textures.

Main Methods:

Utilized wavelets for computing single- and multiple-scale roughness features at different resolutions.
Employed directional wavelets to extract features in multiple orientations.
Transformed feature vectors to be rotational invariant while preserving directional texture information.
Applied iterative K-means for segmentation and a Bayesian classifier for classification.

Main Results:

Achieved high-quality texture segmentation performance using the proposed roughness feature set.
Demonstrated a higher classification rate compared to other tested feature vectors.
The feature set maintained insensitivity to variations in absolute illumination and contrast.

Conclusions:

The developed rotational invariant feature set offers significant improvements in texture segmentation and classification.
Combining single- and multiple-scale analysis with directional information enhances textural representation.
The method provides a robust approach for texture analysis, retaining key advantages of FD-based features.