论文标题
用于实施Koul最小距离估计器及其应用于图像分段的快速算法
A Fast Algorithm for Implementation of Koul's Minimum Distance Estimators and Their Application to Image Segmentation
论文作者
论文摘要
基于经验分布函数的最小距离估计方法由于其理想的特性在内，因此很受欢迎。即使统计文献对最小距离估计的研究感到震惊，但其中的大部分仅限于理论发现：只有很少的统计学家对该方法在现实世界中的应用进行了研究。通过本文，我们通过为一个相当具有挑战性且复杂的计算问题提供解决方案，将应用此方法的应用范围扩展到各种应用领域。该论文解决的问题是图像分割，已在各个领域使用。我们提出了一种基于经典最小距离估计理论的新方法，以解决图像分割问题。然后，通过将其与``分割''策略集成在一起的策略来进一步提高所提出的方法的性能。我们证明，当提出的方法与分割策略结合在一起时，将其应用于复杂的真实图像（例如磁共振图像）时成功地完成了分段问题。
Minimum distance estimation methodology based on an empirical distribution function has been popular due to its desirable properties including robustness. Even though the statistical literature is awash with the research on the minimum distance estimation, the most of it is confined to the theoretical findings: only few statisticians conducted research on the application of the method to real world problems. Through this paper, we extend the domain of application of this methodology to various applied fields by providing a solution to a rather challenging and complicated computational problem. The problem this paper tackles is an image segmentation which has been used in various fields. We propose a novel method based on the classical minimum distance estimation theory to solve the image segmentation problem. The performance of the proposed method is then further elevated by integrating it with the ``segmenting-together" strategy. We demonstrate that the proposed method combined with the segmenting-together strategy successfully completes the segmentation problem when it is applied to the complex, real images such as magnetic resonance images.
