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Depth Edge Filtering Using Parameterized Structured Light Imaging.

Ziqi Zheng1, Seho Bae2, Juneho Yi3

  • 1College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea. zqseria@skku.edu.

Sensors (Basel, Switzerland)
|April 4, 2017
PubMed
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This study introduces a new method for parameterized depth edge detection using structured light imaging. It accurately identifies depth edges while effectively removing shadow regions, outperforming existing techniques.

Area of Science:

  • Computer Vision
  • 3D Imaging
  • Optical Metrology

Background:

  • Depth edge detection is crucial for 3D scene understanding.
  • Existing methods struggle with shadow regions, leading to inaccurate edge detection.
  • Structured light imaging offers precise depth information but requires robust processing.

Purpose of the Study:

  • To develop a parameterized depth edge detection method for structured light imaging.
  • To effectively handle and remove shadow regions that cause double edges.
  • To provide simpler parameter control for depth edge detection.

Main Methods:

  • Utilizes a single color stripes pattern and a binary stripes pattern for structured light imaging.
  • Employs statistical learning for shadow region identification and removal.
Keywords:
depth detectiondepth edge filterstructured light

Related Experiment Videos

  • Implements parameterized depth edge detection to identify edges within a specified distance range and depth difference.

    • Main Results:

    • Successfully removes shadow regions, preventing the formation of double edges.
    • Demonstrates superior depth edge filtering performance compared to state-of-the-art methods and Kinect depth maps.
    • Achieves more accurate detection of desired depth edges.

    Conclusions:

    • The proposed parameterized depth edge detection method is highly effective for structured light imaging.
    • The technique significantly improves accuracy by addressing limitations of previous methods, particularly shadow regions.
    • Offers a robust and simpler approach to depth edge detection in 3D vision applications.