School of Computing and Communications, Universityof Technology, Sydney, NSW 2007, Australia.
IEEE Trans Image Process. 2010 Jul;19(7):1673-82. doi: 10.1109/TIP.2010.2045071. Epub 2010 Mar 8.
In this paper, we seek to fit a model, specified in terms of connected ellipses, to an image silhouette. Some algorithms that have attempted this problem are sensitive to initial guesses and also may converge to a wrong solution when they attempt to minimize the objective function for the entire ellipse structure in one step. We present an algorithm that overcomes these issues. Our first step is to temporarily ignore the connections, and refine the initial guess using unconstrained Expectation-Maximization (EM) for mixture Gaussian densities. Then the ellipses are reconnected linearly. Lastly, we apply the Levenberg-Marquardt algorithm to fine-tune the ellipse shapes to best align with the contour. The fitting is achieved in a hierarchical manner based upon the joints of the model. Experiments show that our algorithm can robustly fit a complex ellipse structure to a corresponding shape for several applications.
在本文中,我们试图根据连接的椭圆来拟合模型到图像轮廓。一些尝试解决此问题的算法对初始猜测很敏感,并且当它们试图一步最小化整个椭圆结构的目标函数时,也可能会收敛到错误的解决方案。我们提出了一种克服这些问题的算法。我们的第一步是暂时忽略连接,并使用无约束期望最大化(EM)算法对混合高斯密度进行初始猜测的细化。然后,重新线性连接椭圆。最后,我们应用列文伯格-马夸尔特算法(Levenberg-Marquardt algorithm)来微调椭圆形状,使其与轮廓最佳对齐。拟合是基于模型的关节以分层的方式进行的。实验表明,我们的算法可以稳健地将复杂的椭圆结构拟合到几个应用的相应形状。