Circle detection is an important issue that has not been perfectly solved in automated image analysis to date. It is traditionally carried out via pixel-based 3D voting algorithms, involving tremendous computation and requiring huge storage space with questionable accuracy. In this report, a novel edge-section-based 1D voting algorithm is developed in circle detection to improve the detection rate and precision. Based on experiments with simulated image data and a ground-tested standard dataset, the novel scheme significantly outperformed all previous state-of-the-art schemes in detection rate and precision, and was comparable to the state of the art in processing speed.
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
IAS performance data come from Table 2 of Al-Khaffaf et al. [24].
RANSAC performance data come from Table 2 of Al-Khaffaf et al. [24].
FACILE performance data come from Table 2 of Wu et al. [25].
First, second, and third rows of each image correspond to 200, 300, and 400 DPI (dots per inch) (P061-400 DPI image is not included because of the lack of correct ground-truth data).
Highest VRI score in each resolution of the images is shown in bold.
Table 3.
Processing Time Comparison among Four Algorithms on the Standard Dataset
IAS performance data come from Table 2 of Al-Khaffaf et al. [24].
RANSAC performance data come from Table 2 of Al-Khaffaf et al. [24].
FACILE performance data come from Table 2 of Wu et al. [25].
First, second, and third rows of each image correspond to 200, 300, and 400 DPI (dots per inch) (P061-400 DPI image is not included because of the lack of correct ground-truth data).
Highest VRI score in each resolution of the images is shown in bold.
Table 3.
Processing Time Comparison among Four Algorithms on the Standard Dataset