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Related Experiment Video

Updated: Mar 22, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Making texture descriptors invariant to blur.

Michael Gadermayr¹, Andreas Uhl²

¹Institute of Imaging and Computer Vision, RWTH Aachen University, Kopernikusstr. 16, Aachen, 52074 Germany.

EURASIP Journal on Image and Video Processing

|April 13, 2016

Summary

This summary is machine-generated.

This study enhances texture feature extraction by making descriptors robust to image blur. By increasing blur during training, methods achieve higher accuracy without losing distinctiveness, improving real-world applicability.

Keywords:

Feature extraction Invariance Robustness Texture recognition

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Quantifying Intermembrane Distances with Serial Image Dilations

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Published on: September 28, 2018

Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Texture feature extraction methods often lack robustness to real-world image degradations like blur.
State-of-the-art methods struggle with blur when trained on idealized data, but perform better when training data is similarly degraded.

Purpose of the Study:

To develop texture descriptors invariant to blur.
To improve the robustness of texture feature extraction for practical applications.

Main Methods:

Estimated blur measure to determine the level of blur to apply to training images.
Artificially increased blur in training data to match expected real-world degradations.
Evaluated method on synthetically degraded data.

Main Results:

Achieved a high degree of blur invariance in texture descriptors.
Maintained significant distinctiveness of features.
Demonstrated effectiveness beyond ideal Gaussian blur.

Conclusions:

The proposed method enhances texture descriptor robustness to blur.
This approach improves the performance of texture analysis in the presence of image degradations.
The technique is applicable to various types of blur, not just Gaussian.