Zhao Mingyang, Jia Xiaohong, Fan Lubin, Liang Yuan, Yan Dong-Ming
IEEE Trans Image Process. 2021;30:3828-3843. doi: 10.1109/TIP.2021.3065799. Epub 2021 Mar 25.
Fitting ellipses from unrecognized data is a fundamental problem in computer vision and pattern recognition. Classic least-squares based methods are sensitive to outliers. To address this problem, in this paper, we present a novel and effective method called hierarchical Gaussian mixture models (HGMM) for ellipse fitting in noisy, outliers-contained, and occluded settings on the basis of Gaussian mixture models (GMM). This method is crafted into two layers to significantly improve its fitting accuracy and robustness for data containing outliers/noise and has been proven to effectively narrow down the iterative interval of the kernel bandwidth, thereby speeding up ellipse fitting. Extensive experiments are conducted on synthetic data including substantial outliers (up to 60%) and strong noise (up to 200%) as well as on real images including complex benchmark images with heavy occlusion and images from versatile applications. We compare our results with those of representative state-of-the-art methods and demonstrate that our proposed method has several salient advantages, such as its high robustness against outliers and noise, high fitting accuracy, and improved performance.
从不明数据中拟合椭圆是计算机视觉和模式识别中的一个基本问题。基于经典最小二乘法的方法对异常值很敏感。为了解决这个问题，在本文中，我们提出了一种新颖且有效的方法，称为分层高斯混合模型（HGMM），用于在基于高斯混合模型（GMM）的有噪声、包含异常值和遮挡的情况下进行椭圆拟合。该方法分为两层构建，以显著提高其对包含异常值/噪声的数据的拟合精度和鲁棒性，并且已被证明能有效缩小核带宽的迭代间隔，从而加速椭圆拟合。我们对包含大量异常值（高达60%）和强噪声（高达200%）的合成数据以及包含具有严重遮挡的复杂基准图像和来自各种应用的图像的真实图像进行了广泛实验。我们将我们的结果与代表性的最新方法的结果进行比较，并证明我们提出的方法具有几个显著优点，例如其对异常值和噪声的高鲁棒性、高拟合精度以及改进的性能。
