Circle detection on images by line segment and circle completeness

Circle detection from digital images is a necessary operation in many robotics and computer vision tasks to facilitate shape and object recognition. We propose and analyze a novel method, based on line segment detection and circle completeness verification, to detect circles in images. The key idea is to use line segments instead of raw edge pixels to get the circle candidates followed by a verification step to measure the circle's completeness. Experimental results on several synthesized and hand-sketched as well as natural images with various complication favor the accuracy, robustness and efficiency of our approach against other well-known techniques. Our method can deal with incomplete, cocentric, discontinuous and occluded circles with noise and deformation. Moreover, in this paper, we create CDBD, the first benchmark dataset for circle detection with ground truth circles labeled by human, which will establish standard quantitative results in future research regarding circle detection.

1. Introduction

Automatic circle detection is a fundamental problem in computer vision and has a wide variety of applications such as traffic sign detection, robot vision, pupil and iris localization, vectorization of hand-sketched drawings, automatic inspection of manufactured products and components, people counting in surveillance video, etc. In consequence, the circle extraction problem has been extensively studied in the literature and most of the proposed methods belong to either of the two categories, Circular Hough Transform (CHT) and Random Sampling Consensus (RANSAC).