In inertia confinement fusion (ICF) experiments

the visual measurement accuracy and speed of targets directly affect the success rate. Since important imaging feature of several kinds of targets are elliptical

a fast and effective ellipse detection algorithm is required for the targets visual measurement. However

the vague contours and irregular brightness of target images pose great challenges to conventional ellipse detection approaches. Meanwhile

considering the real-time requirement

a new fast ellipse detection algorithm is proposed to solve the problems mentioned above. The ellipse detection process includes two parts: extracting edge pixels of ellipses

and then extracting the ellipse parameters fitting these edge pixels. In order to obtain accurate edges pixels of ellipse

a feature that edge pixels of ellipse have large gray change rate in the direction

which connects the edge pixel and the center of ellipse

is used. The new ellipse-edge detection algorithm is called polar coordinates edge detection (PCED). First

a downscaling target image is used to pre-estimate the center of the ellipse with a conventional ellipse detection method. Second

a polar coordinates system is built

and the origin point of the system is the pre-estimate center of the ellipse. Last

PCED finds the pixels with extreme gray change rate in the ray

which starts from the origin point of the polar coordinates system

as the detected edge pixels. Furthermore

PCED keeps the edge detection in the local region of an image to guarantee the real-time requirement and the effectiveness. Once the edge pixels are detected by PCED

an adaptive ellipse parameters extraction algorithm based on RANSAC is adopted to get the ellipse parameters fitted to the edge pixels. The proposed ellipse parameters extraction algorithm adopts cluster analysis in ellipse parameters space to choose the optimal estimated ellipse parameters. Then

the consistent pixels of the optimal estimated ellipse parameters are chose from the detected ellipse edge pixels. Finally

the results of the ellipse parameters are calculated by the consistent pixels using least square method. The results of comparison experiment between PCED algorithm and Canny algorithm show that the PCED algorithm could achieve more accurate and more effective ellipse edge pixels compared with Canny algorithm

which also makes the following ellipse parameters extracting process more easily. The experiment results of ellipse detection for practical target images show that the processing speed of the proposed algorithm is about 110 ms for one image

which is a significantl increase compared to other conventional algorithms. Moreover

the proposed algorithm also has good performance in repeatability and consistency test experiments. First

the proposed ellipse detection algorithm uses the PCED algorithm to gain the ellipse edge pixels effectively. Then

the proposed ellipse detection algorithm adopts the adaptive ellipse parameters extraction algorithm based on RANSAC to calculate the ellipse parameters fitting to the detected edge pixels. Taking the advantages of the two processes

the proposed algorithm could gain numerically small but effective ellipse edge pixels

and then could get accurate ellipse parameters fast. The comparison experiments demonstrate that the proposed method has advantages of low time consumption

high accuracy

and good robustness

compared to other conventional methods.