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

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Published on: September 2, 2020

Fast template matching based on normalized cross correlation with adaptive multilevel winner update.

Shou-Der Wei¹, Shang-Hong Lai

¹Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan, R.O.C.

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

|November 1, 2008

Summary

This study introduces a fast pattern matching algorithm using normalized cross correlation (NCC) and an adaptive multilevel partition. The method efficiently skips calculations for quicker image matching, even in varied lighting.

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Area of Science:

Computer Vision
Image Processing
Algorithm Design

Background:

Normalized Cross Correlation (NCC) is a standard criterion for image matching.
Efficient algorithms are needed to reduce computational load in pattern matching.
Existing methods may struggle with variations in lighting conditions.

Purpose of the Study:

To develop a fast and efficient pattern matching algorithm.
To improve the search speed of Normalized Cross Correlation (NCC).
To enhance image matching performance under diverse lighting conditions.

Main Methods:

Proposed a novel algorithm combining adaptive multilevel partition and a winner update scheme.
Utilized an upper bound for NCC derived from the Cauchy-Schwarz inequality.
Partitioned cross-correlation summation based on gradient energies for optimized search order.

Main Results:

The algorithm demonstrates significant efficiency in pattern matching.
The winner update scheme effectively skips unnecessary calculations.
Successful image matching was achieved across various lighting conditions.

Conclusions:

The proposed algorithm offers a computationally efficient solution for image matching.
Adaptive multilevel partition and winner update scheme enhance NCC performance.
The algorithm is robust for pattern matching in environments with changing illumination.