Parallel thinning with two-subiteration algorithms
Communications of the ACM
Detecting partially occluded ellipses using the Hough transform
Image and Vision Computing - 4th Alvey Vision Meeting
A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decomposable parameter space for the detection of ellipses
Pattern Recognition Letters
Randomized Hough transform: improved ellipse detection with comparison
Pattern Recognition Letters
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Fitting of Surfaces to Data with Covariances
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Primitive Extraction Using a Genetic Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hierarchical approach for fast and robust ellipse extraction
Pattern Recognition
A unifying view on dataset shift in classification
Pattern Recognition
Edge curvature and convexity based ellipse detection method
Pattern Recognition
A precise ellipse fitting method for noisy data
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
EDCircles: A real-time circle detector with a false detection control
Pattern Recognition
Geometric property based ellipse detection method
Journal of Visual Communication and Image Representation
Hi-index | 0.01 |
Detection of multiple ellipses in noisy environments is a basic yet challenging task in many vision related problems. The key area of difficulty is on distinguishing the pixels pertaining to each target in the presence of noise. To tackle with the issue, we propose a hierarchical approach which is motivated by the fact that any segment of an ellipse can identify itself in ellipse reconstruction. First, we find all the neat edges without any branches, followed by an ellipse fitting on each of them. Second, some target candidates are estimated based on the neat edges, by a proposed grouping strategy. Finally, the targets are detected based on the candidates, by a proposed selective competitive algorithm to distinguish the true pixels of each target. A real application of the proposed method is illustrated in addition to some other demonstrative experiments.