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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

Statistical Hough Transform.

Rozenn Dahyot¹

¹Department of Statistics, Trinity College, Dublin, Ireland. Rozenn.Dahyot@tcd.ie

IEEE Transactions on Pattern Analysis and Machine Intelligence

|June 23, 2009

Summary

This study introduces a new method for image processing, moving beyond traditional histograms to continuous kernel estimates. This approach improves density estimation, making it more robust and memory-efficient for complex image alignment tasks.

Area of Science:

Image Processing
Computer Vision
Statistical Modeling

Background:

Standard Hough Transform (SHT) traditionally uses histograms for estimation.
Histogram-based density estimation is memory-intensive and sparse in high dimensions.

Purpose of the Study:

To extend the Hough Transform formulation to continuous kernel estimates.
To develop a more robust, memory-efficient, and unsupervised statistical framework for image analysis.

Main Methods:

Utilizing continuous kernel estimates instead of histograms.
Incorporating dependencies between variables for density estimation.
Developing an unsupervised framework with automatic parameter estimation.

Main Results:

Related Experiment Videos

Last Updated: Jun 22, 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

The proposed method is robust to noise and insensitive to spatial coordinate origins.
The new modeling effectively encodes image alignment content.
Demonstrated improved performance over traditional histogram-based methods.

Conclusions:

Continuous kernel estimates offer a superior alternative to histograms for Hough Transform.
The unsupervised and flexible framework enhances image analysis capabilities.
This approach provides better encoding of alignment information in images.