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Robust ellipse detection based on hierarchical image pyramid and Hough transform
Chung-Fang Chien1, Yu-Che Cheng, Ta-Te Lin
1Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106, Taiwan.
Summary
This study introduces a fast and robust ellipse detection algorithm using a multipass Hough transform and image pyramid. The method significantly reduces false positives and computational costs for accurate ellipse identification in images.
Area of Science:
- Computer Vision
- Image Processing
- Pattern Recognition
Background:
- Ellipse detection is crucial in image analysis.
- Conventional methods like randomized Hough transform can be computationally expensive and prone to false positives.
Purpose of the Study:
- To develop a fast and robust ellipse detection algorithm.
- To reduce computational complexity and false positive rates compared to existing methods.
Main Methods:
- Utilizes a multipass Hough transform combined with an image pyramid data structure.
- Iteratively refines ellipse detection from low to high image resolution.
- Applies Hough transform in multiple passes to find remaining ellipses.
Main Results:
- Achieves a computing complexity of Θ(n(5/2)), reducing computation time and memory.
- Demonstrates robustness to noise and varying ellipse sizes.
- Reports average detection accuracies above 90% for fewer ellipses, decreasing slightly for more.
- Maintains an average false positive error rate below 2%.
Conclusions:
- The proposed algorithm offers a significant improvement in speed and accuracy for ellipse detection.
- It effectively minimizes false positives and is robust in noisy conditions.
- This method provides an efficient solution for identifying ellipses in digital images.
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