Robust regression and outlier detection
Robust regression and outlier detection
Large sample bias in least squares estimators of a circular arc center and its radius
Computer Vision, Graphics, and Image Processing
Segmentation of edges into lines and arcs
Image and Vision Computing
Characterization of image degradation caused by scanning
Pattern Recognition Letters
Constrained Hough transforms for curve detection
Computer Vision and Image Understanding
An efficient randomized algorithm for detecting circles
Computer Vision and Image Understanding
Robust and Accurate Vectorization of Line Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Symbol recognition using spatial relations
Pattern Recognition Letters
Determining Digital Circularity Using Integer Intervals
Journal of Mathematical Imaging and Vision
EDCircles: A real-time circle detector with a false detection control
Pattern Recognition
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
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In this paper we present a method to robustly detect circular arcs in a line drawing image. The method is fast, robust and very reliable, and is capable of assessing the quality of its detection. It is based on Random Sample Consensus minimization, and uses techniques that are inspired from object tracking in image sequences. It is based on simple initial guesses, either based on connected line segments, or on elementary mainstream arc detection algorithms. Our method consists of gradually deforming these circular arc candidates as to precisely fit onto the image strokes, or to reject them if the fitting is not possible, this virtually eliminates spurious detections on the one hand, and avoiding nondetections on the other hand.