目的 针对惯性约束核聚变实验中靶图像轮廓模糊、亮度不均匀等问题，从提高图像处理实时性角度出发，提出一种高可靠性和高精度的快速椭圆检测方法。方法 首先利用椭圆边缘点在它与圆心相连方向上具有较大灰度变化率这一特点，以预估中心点为极点建立极坐标系，通过从极点出发的射线上灰度变化率极值点搜索实现椭圆边缘点检测，极值点搜索在图像局部范围进行保证边缘点检测的有效性和实时性；其次利用基于RANSAC的自适应椭圆参数提取算法得到最终椭圆参数，该方法利用椭圆参数空间聚类分析选取最优椭圆参数，从而实现了一致样本集的自适应选择，在保证了椭圆参数拟合精度的同时提高了算法的适应性和鲁棒性。结果 采用本文算法检测一幅图像的平均时间约为110 ms，与常用椭圆检测方法相比检测速度有显著提高。结论 对比实验结果表明，本文提出的椭圆检测方法与其他方法相比具有更高的精度、更快的实时性和更强的鲁棒性。
Fast ellipse detection for target images in ICF experiment
Objective 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. Method 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. Result 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. Conclusion 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.