Ellipse Detection and Fitting Techniques in Computer Vision
Summary
Ellipse detection and fitting form a cornerstone of geometric computer vision, underpinning tasks from camera calibration to object recognition. The process typically begins with edge or contour extraction, followed by methods that identify and parameterise elliptical shapes within noisy or cluttered imagery. Classical approaches leverage the Hough transform to accumulate votes in a parameter space, while algebraic and geometric fitting techniques estimate ellipse parameters by minimising error measures. Robust estimators and sample consensus schemes address outliers and partial occlusions, and hybrid strategies integrate region growing or merging of arc segments to improve efficiency. Contemporary developments emphasise numerical stability, reduced computational burden and real-time performance, enabling applications in robotics, industrial inspection and medical imaging. The interplay between accurate parameter estimation and algorithmic speed remains central, with new solvers exploiting minimal sample sets or multistage optimisation to balance precision and responsiveness.
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Ellipse Detection and Fitting Techniques in Computer Vision publication trend
The graph below shows the total number of articles in ellipse detection and fitting techniques in computer vision across all publications each year (not limited to Nature Index journals).
Technical terms
Contour points: Discrete pixels or points outlining the boundary of a shape, used as input for ellipse fitting algorithms.
Ellipse parameterisation: Representation of an ellipse by its centre coordinates, semi-major and semi-minor axes and orientation angle.
Least squares fitting: Optimisation technique that minimises the sum of squared residuals between observed data points and the model curve.
Algebraic distance: Error measure based on the polynomial equation of an ellipse, often used for computational efficiency.
Geometric distance: True Euclidean distance from a data point to the nearest point on the ellipse, offering higher accuracy at increased cost.
Random Sample Consensus (RANSAC): Iterative method that fits models to subsets of data and identifies inliers to robustly estimate parameters in the presence of outliers.
References
- A Minimal Solution for Image-Based Sphere Estimation. International Journal of Computer Vision (2023).
- A Fast and Robust Ellipse‐Detection Method Based on Sorted Merging. The Scientific World JOURNAL (2014).
- A Method for Measuring Parameters of Defective Ellipse Based on Vision. Sensors (2023).
- A Robust Real-Time Ellipse Detection Method for Robot Applications. Drones (2023).
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