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Related Experiment Video

Updated: May 29, 2026

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography
09:00

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

A theoretical comparison of texture algorithms.

R W Conners1, C A Harlow

  • 1MEMBER, IEEE, Department of Electrical Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study evaluated four texture analysis algorithms for automatic texture discrimination. The spatial gray level dependence method (SGLDM) proved most effective, outperforming others in extracting texture information.

Related Experiment Videos

Last Updated: May 29, 2026

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography
09:00

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

Area of Science:

  • Image analysis and computer vision
  • Texture analysis and pattern recognition
  • Signal processing

Background:

  • Automatic texture discrimination is crucial for image analysis tasks.
  • Several algorithms exist, but their comparative performance in extracting texture information is not fully understood.
  • Previous research has explored texture perception using specific texture models.

Purpose of the Study:

  • To evaluate and compare the efficacy of four texture analysis algorithms: spatial gray level dependence method (SGLDM), gray level run length method (GLRLM), gray level difference method (GLDM), and power spectral method (PSM).
  • To assess the amount of texture-context information captured by each algorithm's representative matrices or functions.
  • To generalize findings from Markov textures to a broader class of translation stationary random fields.

Main Methods:

  • The evaluation focused on the intrinsic information content of texture representations, independent of specific feature sets or pattern recognition schemes.
  • Comparison involved two stages: first, using Markov-generated textures, and second, extending results to translation stationary random fields of order two.
  • Analysis centered on the information contained within spatial gray level dependence matrices, gray level run length matrices, gray level difference density functions, and power spectra.

Main Results:

  • The spatial gray level dependence method (SGLDM) demonstrated superior performance among the four algorithms evaluated.
  • The gray level difference method (GLDM) was found to be more powerful than the power spectral method (PSM).
  • The study established a hierarchy of algorithm effectiveness based on their ability to discriminate textures.

Conclusions:

  • The SGLDM is the most effective algorithm for automatic texture discrimination among the methods investigated.
  • The GLDM offers better texture discrimination capabilities compared to the PSM.
  • The findings provide valuable insights for selecting appropriate texture analysis techniques in various applications.