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Related Concept Videos

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  5. Information And Computing Sciences
  7. Artificial Intelligence
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  11. Evaluating Feature-based Homography Pipelines For Dual-camera Registration In Acupoint Annotation.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Artificial Intelligence
  9. Natural Language Processing
  11. Evaluating Feature-based Homography Pipelines For Dual-camera Registration In Acupoint Annotation.

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Evaluating Feature-Based Homography Pipelines for Dual-Camera Registration in Acupoint Annotation.

Thathsara Nanayakkara1, Hadi Sedigh Malekroodi1, Jaeuk Sul2

  • 1Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Republic of Korea.

Journal of Imaging
|November 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a low-cost dual-camera system for precise acupoint localization in traditional Korean medicine. It enhances artificial intelligence and extended reality tools by standardizing point visibility for reliable data acquisition.

Keywords:
acupoint localizationdual-camera systemfeature matchingimage registration

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Traditional Korean Medicine

Background:

  • Acupoint localization is critical for AI/XR in traditional Korean medicine.
  • Current 2D image annotation methods exhibit significant inter- and intra-annotator variability.
  • Standardized and accurate acupoint identification is needed for technological advancements.

Purpose of the Study:

  • To develop a low-cost, dual-camera imaging system for reliable acupoint localization.
  • To fuse infrared (IR) and RGB views for enhanced point visibility and standardization.
  • To evaluate feature detection and matching algorithms for robust homography estimation on a Raspberry Pi 5 platform.

Main Methods:

  • A dual-camera system integrating IR and RGB imaging on a Raspberry Pi 5.
infrared imaging (IR)
  • Utilized an IR ink pen and 780 nm emitter array for standardized acupoint marking.
  • Evaluated five feature detectors (SIFT, ORB, KAZE, AKAZE, BRISK) with two matchers (FLANN, BF) for homography pipelines.
  • Assessed system performance across varying camera distances and hand postures.

    • Main Results:

    • IR ink demonstrated superior contrast and visibility under IR illumination for acupoint detection.
    • KAZE + FLANN achieved the lowest mean 2D error (1.17 ± 0.70 px) and aspect-aware error (0.08 ± 0.05%).
    • Dual-camera registration maintained mean 2D error below ~3 px and aspect-aware error below ~0.25% across different postures.

    Conclusions:

    • The proposed dual-camera system offers a practical and cost-effective solution for high-quality acupoint dataset generation.
    • This framework supports the development of AI-based localization and XR integration in traditional Korean medicine.
    • The system demonstrates stable, reproducible performance suitable for automated acupuncture education systems.