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

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

Published on: January 5, 2024

Automated defect detection system using wavelet packet frame and Gaussian mixture model.

Soo Chang Kim¹, Tae Jin Kang

¹Intelligent Textile System Research Center and School of Materials Science and Engineering, Seoul National University, Seoul, Korea 151-744. loveincarnate@hotmail.com

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|October 19, 2006

Summary

This study introduces automated defect detection for textiles using texture analysis. The method achieves high accuracy in identifying textile flaws with both supervised and unsupervised approaches.

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Area of Science:

Materials Science
Computer Vision
Image Processing

Background:

Automated defect detection in textiles is crucial for quality control.
Texture analysis offers a promising approach for identifying subtle flaws in homogeneous materials.

Purpose of the Study:

To propose and validate an automated method for defect detection and localization in homogeneous textiles.
To compare the performance of supervised and unsupervised defect detection techniques.

Main Methods:

Texture features were extracted using wavelet packet frame decomposition and Karhunen-Loève transform.
A Gaussian mixture model was employed for pixel-wise defect classification.
Unsupervised detection utilized Kullback-Leibler divergence for subblock heterogeneity identification.

Related Experiment Videos

Last Updated: Jul 19, 2026

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

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

Published on: January 5, 2024

Main Results:

The proposed method achieved visually accurate segmentation of defects.
Excellent detection rates and low false alarm rates were observed for both supervised and unsupervised methods.
The approach demonstrated effectiveness across 25 diverse textile image pairs.

Conclusions:

The developed approach is valid and effective for automated textile defect detection and localization.
Texture analysis combined with Gaussian mixture models provides a robust solution for quality control in textile manufacturing.