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Published on: February 23, 2017
Subspace-based and DIRECT algorithms for distorted circular contour estimation
Julien Marot1, Salah Bourennane
1Institut Fresnel/CNRS UMR 6133-Domaine Universitaire de Saint Jérôme F-13397 Marseille Cedex 20, France. julien.marot@fresnel.fr
Summary
This study introduces a novel method for estimating multiple radii and distorted circular contours in digital images. The approach enhances circle detection for complex shapes and multiple objects.
Area of Science:
- Digital Image Processing
- Computer Vision
- Signal Processing
Background:
- Accurate detection of circular features is crucial in various digital image processing applications.
- Existing methods like subspace-based line detection (SLIDE) primarily focus on single circle estimation.
- Challenges remain in detecting multiple, non-concentric circles and distorted circular contours.
Purpose of the Study:
- To develop a novel method for estimating multiple radii of circular features.
- To extend circle estimation techniques for retrieving circular-like distorted contours.
- To provide a robust approach for segmenting free-form objects and detecting multiple non-concentric circles and rotated ellipses.
Main Methods:
- A new model for virtual signal generation simulating a circular antenna is developed.
- Circle center estimation utilizes the subspace-based line detection (SLIDE) method.
- Radius estimation employs a high-resolution method based on variable speed propagation and linear phase signals.
- Free-form object segmentation is achieved using gradient methods or a combination of dividing rectangles and spline interpolation.
Main Results:
- The proposed method successfully estimates multiple radii and retrieves distorted circular contours.
- Performance is validated against established methods including least-squares, Hough transform, and gradient vector flow.
- The approach demonstrates effectiveness on both synthetic and real-world image data.
Conclusions:
- The novel method significantly advances circular feature detection in digital images.
- It offers a robust solution for segmenting complex shapes and identifying multiple circular and elliptical objects.
- This work provides a valuable tool for applications requiring precise geometric feature extraction.
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