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This study introduces an improved method for unmanned aerial vehicle (UAV) image stitching to eliminate ghosting and seams. The new approach uses dynamic programming for optimal seam-line searching, enhancing panoramic image quality.

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Area of Science:

  • Computer Vision
  • Photogrammetry
  • Robotics

Background:

  • Creating seamless mosaics from Unmanned Aerial Vehicle (UAV) imagery is crucial for various applications.
  • Ghosting and seam artifacts are significant challenges that degrade the quality of UAV image mosaics.
  • Existing methods often struggle to accurately identify optimal seam-lines, leading to visual distortions.

Purpose of the Study:

  • To develop an improved seam-line searching method for Unmanned Aerial Vehicle (UAV) image mosaicking.
  • To address and mitigate the issues of ghosting and seams in panoramic UAV images.
  • To enhance the accuracy and visual quality of UAV-based image mosaics.

Main Methods:

  • Feature extraction and matching between adjacent UAV images to align them in a common coordinate system.
  • Calculation of grayscale difference, gradient minimum, and optical flow in overlapped image regions to define an energy function.
  • Application of an improved dynamic programming algorithm with adaptive energy aggregation and traversal for optimal seam-line identification.

Main Results:

  • The proposed method effectively identifies optimal seam-lines, avoiding problematic ground objects.
  • Experimental results demonstrate a significant reduction in ghosting and seam artifacts in the final mosaics.
  • The enhanced dynamic programming approach leads to more ideal splicing paths for adjacent UAV images.

Conclusions:

  • The developed method provides an effective solution for ghosting and seam problems in UAV image mosaicking.
  • The improved seam-line searching technique enhances the overall quality and reliability of panoramic UAV imagery.
  • This advancement contributes to better aerial data acquisition and analysis through high-quality image mosaics.