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Line detection in images through regularized Hough transform.

Nitin Aggarwal1, William Clem Karl

  • 1Department of Electical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. aggarwal@uiuc.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 8, 2006
PubMed
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This study introduces a novel inverse problem approach for detecting straight lines in grayscale images, enhancing traditional Hough transform methods with regularization for improved accuracy and efficiency in computer vision applications.

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Imaging

Background:

  • Traditional Hough transform is effective for binary images but limited for grayscale.
  • Detecting lines in grayscale images presents challenges due to noise and intensity variations.
  • Existing methods often struggle with accuracy and computational efficiency in complex scenes.

Purpose of the Study:

  • To develop a robust method for detecting straight lines in grayscale images.
  • To enhance the performance of Hough-based line detection using regularization.
  • To formulate line detection as an inverse problem solvable with the inverse Radon operator.

Main Methods:

  • Formulating grayscale line detection as an inverse problem using the inverse Radon operator.

Related Experiment Videos

  • Applying regularization techniques to incorporate prior information and improve Hough transform performance.
  • Deriving efficient computational schemes for solving optimization problems in large-scale applications.
  • Interpreting the approach as finding an optimal image representation using a dictionary of lines.

    • Main Results:

    • The proposed inverse problem formulation enhances line detection accuracy in grayscale images.
    • Regularization effectively incorporates prior information, boosting Hough-based detector performance.
    • Efficient computational schemes enable the application of this method to large datasets.
    • The approach offers an alternative perspective linking Hough transforms to adaptive signal representation.

    Conclusions:

    • The inverse problem approach with regularization offers a significant advancement for line detection in grayscale images.
    • This method improves upon traditional Hough transform techniques by handling noise and intensity variations more effectively.
    • The framework provides a flexible and efficient solution for real-world computer vision and image processing tasks.