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Related Experiment Video

Updated: Oct 22, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Identification of QR Code Perspective Distortion Based on Edge Directions and Edge Projections Analysis.

Ladislav Karrach1, Elena Pivarčiová1, Pavol Božek2

  • 1Technical University in Zvolen, Faculty of Technology, Department of Manufacturing and Automation Technology, Masarykova 24, 960 01 Zvolen, Slovakia.

Journal of Imaging
|August 30, 2021
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Summary

Quick Response (QR) Codes are versatile 2D matrix codes. This study compares QR Code localization algorithms and introduces a novel method for detecting perspective distortion, efficient for real-time applications.

Keywords:
QR Code recognitionQuick Response Code localizationadaptive thresholdingedge projectionsfinder patternperspective distortion

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Quick Response (QR) Codes are widely adopted 2D matrix codes, offering superior data capacity over 1D barcodes.
  • Efficient localization of multiple QR Codes in images is crucial for various applications.
  • Identifying and correcting perspective distortion in QR Codes is essential for reliable data extraction.

Purpose of the Study:

  • To compare existing algorithms for localizing multiple QR Codes using standard finder patterns.
  • To introduce a novel, computationally efficient algorithm for detecting perspective distortion in QR Codes.
  • To evaluate the algorithm's performance on low-resolution images and its suitability for real-time processing.

Main Methods:

  • Comparative analysis of QR Code localization algorithms based on finder pattern detection.
  • Development of a novel algorithm analyzing edge orientations and projection statistics to identify perspective distortion.
  • Evaluation of algorithm efficiency and accuracy in simulated and real-world scenarios.

Main Results:

  • The novel algorithm effectively identifies perspective distortion by analyzing edge directions and projection characteristics.
  • The proposed method demonstrates computational efficiency, making it suitable for real-time QR Code processing.
  • The algorithm performs well even with low-resolution images, enhancing robustness.

Conclusions:

  • A new, efficient algorithm for detecting perspective distortion in QR Codes has been developed.
  • This method enhances the reliability of QR Code reading in challenging imaging conditions.
  • The algorithm's efficiency and robustness support its application in real-time systems.