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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

Fast template matching with polynomials.

Shinichiro Omachi¹, Masako Omachi

¹Graduate School of Engineering, Tohoku University, Sendai-shi 980-8579, Japan. machi@ecei.tohoku.ac.jp

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 11, 2007

Summary

This study introduces algebraic template matching, a novel algorithm that efficiently finds the most similar image region regardless of size. It uses polynomial approximation for reduced computational cost and improved accuracy in image processing.

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Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Template matching is a fundamental technique in image and signal processing.
Existing methods face challenges with variations in template and image dimensions.

Purpose of the Study:

To propose a novel and efficient template matching algorithm.
To address limitations of existing methods, particularly scale variations.

Main Methods:

Developed Algebraic Template Matching (ATM).
Employs polynomial approximation (specifically Legendre polynomials) of the template image.
Calculates similarities between the template polynomial and input image patches.

Main Results:

The proposed algorithm efficiently detects the most similar partial image at any location, width, and height.
Demonstrated significant reduction in computational cost compared to existing methods.
Improved quality of the approximated image through Legendre polynomial approximation.

Conclusions:

Algebraic template matching offers a computationally efficient and accurate solution for template matching.
The method is particularly effective when template and image patch dimensions differ.
The use of Legendre polynomials enhances approximation quality and reduces computational load.