IEEE Trans Image Process. 2017 Aug;26(8):3665-3679. doi: 10.1109/TIP.2017.2704660. Epub 2017 May 16.
Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis, where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy. The algorithm leverages a newly developed projective invariant to significantly prune the undesired candidates and to pick out elliptical ones. The invariant is able to reflect the intrinsic geometry of a planar curve, giving the value of -1 on any three collinear points and +1 for any six points on an ellipse. Thus, we apply the pruning and picking by simply comparing these binary values. Moreover, the calculation of the invariant only involves the determinant of a 3×3 matrix. Extensive experiments on three challenging data sets with 648 images demonstrate that our detector runs 20%-50% faster than the state-of-the-art algorithms with the comparable or higher precision.
从图像中检测椭圆目标是机器人导航和工业诊断中的核心任务，而检测时间一直是一个关键问题。由于拟合椭圆方程的碎片候选数量（边缘或圆弧）巨大，现有的方法几乎不适用于这些硬件资源有限的实时场景。在本文中，我们提出了一种快速、高精度的椭圆检测算法。该算法利用一种新开发的射影不变量，显著减少了不需要的候选对象，并挑选出椭圆对象。该不变量能够反映平面曲线的内在几何形状，对于任意三个共线点的值为-1，对于椭圆上的任意六个点的值为+1。因此，我们通过简单地比较这些二进制值来进行修剪和挑选。此外，不变量的计算只涉及到一个 3x3 矩阵的行列式。在三个具有 648 张图像的具有挑战性的数据集上进行的广泛实验表明，我们的检测器的运行速度比最先进的算法快 20%-50%，而精度相当或更高。
