支持向量回归在圆形检测中的应用

对圆形的识别是机器视觉中最基本和最重要的任务之一,为了准确确定复杂背景图像中圆的位置,提出了一种将支持向量回归模型与三点拟合圆联合起来的新算法,通过支持向量回归模型训练不同类型的圆形样本,得到超平面方程f(x),以f(x)为中心线,构建一个宽度为2ε的近似圆环型间隔带,在此间隔带上的点都被认为属于圆形边界上的点,然后运用三点拟合圆几何算法计算出圆心和半径,从而达到识别圆形的目的。实验结果表明,联合算法通过对训练样本的学习,能够在噪声比较大的背景图像中得到圆形的边界信息,从而确定圆的位置,较仅使用某一种圆形识别算法有一定的优势。在以圆形作为定位的机器视觉领域,具有重要的理论研究价值与实践意义。

Application of support vector regression in circle detection

Circle detection is one of the most basic and important tasks in machine vision. In order to accurately determine the circle location in complex background images, a new joint algorithm that combines the model of support vector regression with the three-point fitting circle detection algorithm is proposed. The different types of circular samples are trained by the support vector regression model in the algorithm. So the hyperplane equation f(x) can be obtained. Taking the f(x) as the center line, one similar circular ring with the width of 2 can be constructed. The points in this interval are considered as the circular boundary points. Then, the center and radius can be calculated based on the three-point fitting circular geometry algorithm, so as to achieve the purpose of identifying the circle. The experimental results show that the circular boundary information can be obtained from the relatively noisy background images by learning the training samples thereby determining the location of the circle, which has some advantages over using only a certain circular recognition algorithm. In the field of machine vision positioning with circles, this joint algorithm has important theoretical research value and practical significance.