Accurate centerline detection and line width estimation of thick lines using the radon transform

Area of Science:

• Computer Vision
• Image Processing
• Remote Sensing

Background:

• Centerline detection and line width estimation are crucial for applications like road network extraction from high-resolution imagery.
• Radon transform methods offer robustness in noisy images but struggle with thick lines due to peak selection issues.

Purpose of the Study:

• To investigate and address key issues affecting centerline detection of thick lines using the Radon transform.
• To propose a refined methodology for accurate centerline and line width estimation.

Main Methods:

• Investigated issues impacting Radon transform-based centerline detection.
• Proposed a mean filter for accurate peak localization in the Radon image.
• Utilized profile analysis for refining line parameters.
• Addressed and corrected the theta-boundary problem in the Radon transform.

Main Results:

• Successfully identified the true peak in the Radon image using a mean filter.
• Refined line parameters through profile analysis.
• Corrected erroneous line parameters caused by the theta-boundary problem.
• Demonstrated effectiveness in finding the centerline and estimating line width for thick lines.

Conclusions:

• The proposed methodology effectively overcomes limitations of the standard Radon transform for thick line analysis.
• This approach enhances accuracy in centerline detection and line width estimation for challenging datasets.
• The findings are significant for improving automated feature extraction in remote sensing and other computer vision domains.