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Related Experiment Video

Updated: May 29, 2026

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

The first stage in two-stage template matching.

X Li¹, R C Dubes

¹Department of Computer Science, Michigan State University, East Lansing, MI 48824; Wichita State University, Wichita, KS 67208.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study introduces two new statistics, R and G, for binary template matching. Statistic G offers a good balance of computational efficiency and sensitivity for object detection in the first stage of template matching.

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

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

Area of Science:

Computer Vision
Image Processing
Pattern Recognition

Background:

Two-stage binary template matching is crucial for object detection.
The initial stage requires efficient and sensitive methods to identify potential object locations.

Purpose of the Study:

To formulate the first stage of two-stage, binary template matching as a hypothesis testing problem.
To propose and evaluate new statistics for improved object detection performance.

Main Methods:

Formulation of the problem as hypothesis testing, including a 'no object' hypothesis.
Development of two new likelihood ratio-based statistics: R and G.
Comparison with existing methods (sum of absolute differences, correlation) using analytical approximations and Monte Carlo experiments.
Evaluation based on statistical power and sensitivity to object location.

Main Results:

Statistic R demonstrates higher statistical power but is computationally intensive.
Statistic G offers a favorable balance between computational cost and sensitivity to object location.
Statistic G outperforms sum of absolute differences and correlation in both power and sensitivity.
All tested statistics show robustness to incomplete knowledge of distortion parameters.

Conclusions:

Statistic G is recommended for the first stage of two-stage, binary template matching due to its superior sensitivity and power.
The proposed statistics are robust to distortions, enhancing their practical applicability.
Experiments on Landsat images validate the effectiveness of statistic G.