Sukno Federico M, Frangi Alejandro F
Center for Computational Imaging and Simulation Technologies in Biomedicine, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
IEEE Trans Image Process. 2008 Dec;17(12):2442-55. doi: 10.1109/TIP.2008.2006604.
One of the drawbacks of statistical shape models is their occasional failure to converge. Although visually this fact is usually easy to recognize, there is no automatic way to detect it. In this paper, we introduce a generic reliability measure for statistical shape models. It is based on a probabilistic framework and uses information extracted by the model itself during the matching process. The proposed method was validated with two variants of Active Shape Models in the context facial image analysis. Experimental results on more than 3700 facial images showed a high degree of correlation between the segmentation accuracy and the estimated reliability metric.
统计形状模型的缺点之一是它们偶尔会无法收敛。虽然从视觉上看这个问题通常很容易识别，但没有自动检测它的方法。在本文中，我们为统计形状模型引入了一种通用的可靠性度量。它基于概率框架，并使用模型在匹配过程中自身提取的信息。所提出的方法在面部图像分析的背景下，通过主动形状模型的两个变体进行了验证。对3700多张面部图像的实验结果表明，分割精度与估计的可靠性指标之间存在高度相关性。
