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Updated: Jun 24, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Radon representation-based feature descriptor for texture classification.

Guangcan Liu1, Zhouchen Lin, Yong Yu

  • 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. roth@sjtu.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 24, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new Radon representation-based feature descriptor (RRFD) for robust texture classification. RRFD enhances geometric and illumination invariance, outperforming existing methods.

Related Experiment Videos

Last Updated: Jun 24, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Intraclass variation in textures due to geometric transformations and illumination changes poses challenges for robust classification.
  • Existing texture descriptors often lack comprehensive invariance to these variations.

Purpose of the Study:

  • To develop a novel feature descriptor, the Radon representation-based feature descriptor (RRFD), for enhanced texture classification.
  • To address intraclass variations caused by geometric transformations and illumination changes.

Main Methods:

  • The proposed RRFD converts images into a lower-dimensional Radon-pixel representation using the Radon transform.
  • Affine invariance is achieved by projecting Radon-pixel pairs onto an invariant feature space using a ratiogram.
  • Illumination invariance is established through a dedicated distance metric in the invariant feature space.

Main Results:

  • RRFD demonstrates superior affine invariance compared to existing Radon transform-based methods.
  • Experimental results on the CUReT dataset validate RRFD's effectiveness for texture classification.

Conclusions:

  • RRFD is a powerful and robust feature descriptor for texture classification.
  • The method effectively handles geometric and illumination variations, leading to improved classification accuracy.