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Related Concept Videos

Predicting Products: Substitution vs. Elimination02:52

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Multiple Comparison Tests01:13

Multiple Comparison Tests

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Coefficient of Correlation01:12

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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
10:14

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

Published on: September 2, 2020

Correlation-coefficient-based fast template matching through partial elimination.

Arif Mahmood1, Sohaib Khan

  • 1Punjab University College of Information Technology, Lahore 54000, Pakistan. arifm@csse.uwa.edu.authe

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|October 15, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces new partial correlation elimination algorithms for faster template matching. These methods efficiently speed up correlation-based similarity measures, outperforming existing techniques on various data sets.

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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
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Published on: November 3, 2011

Area of Science:

  • Computer Vision
  • Image Processing
  • Algorithm Development

Background:

  • Partial computation elimination is crucial for efficient template matching.
  • Traditional methods struggle with non-monotonic correlation measures.
  • Existing techniques often fall short in accelerating correlation-based similarity.

Purpose of the Study:

  • To adapt partial elimination techniques for correlation coefficients.
  • To develop novel algorithms for fast template matching.
  • To enhance the efficiency of image search and comparison.

Main Methods:

  • Formulating partial elimination for correlation coefficients using a monotonic approach.
  • Proposing basic-mode and extended-mode partial correlation elimination algorithms.
  • Developing strategies for algorithm selection and initial peak correlation estimation.

Main Results:

  • Demonstrated the applicability of partial elimination to correlation coefficients.
  • Introduced basic-mode (efficient for small templates) and extended-mode (efficient for medium/large templates) algorithms.
  • Showcased significant speedups compared to existing fast techniques on real image data.

Conclusions:

  • Partial elimination techniques can be effectively applied to correlation coefficients for fast template matching.
  • The proposed algorithms offer exact accuracy and superior performance across various template sizes.
  • The developed methods provide a significant advancement in accelerating image template matching processes.