利用图割和递归核形状先验追踪图像序列中单调推进的边界。
Tracking monotonically advancing boundaries in image sequences using graph cuts and recursive kernel shape priors.
机构信息
Department of Biomathematics, University of California-Los Angeles, Los Angeles, CA 90025, USA.
出版信息
IEEE Trans Med Imaging. 2012 May;31(5):1008-20. doi: 10.1109/TMI.2011.2178122. Epub 2011 Dec 5.
We introduce a probabilistic computer vision technique to track monotonically advancing boundaries of objects within image sequences. Our method incorporates a novel technique for including statistical prior shape information into graph-cut based segmentation, with the aid of a majorization-minimization algorithm. Extension of segmentation from single images to image sequences then follows naturally using sequential Bayesian estimation. Our methodology is applied to two unrelated sets of real biomedical imaging data, and a set of synthetic images. Our results are shown to be superior to manual segmentation.
我们介绍了一种概率计算机视觉技术，用于跟踪图像序列中物体的单调增长边界。我们的方法结合了一种新的技术，将统计先验形状信息纳入基于图割的分割中，并借助最大化-最小化算法进行辅助。然后，使用顺序贝叶斯估计可以自然地将分割从单张图像扩展到图像序列。我们的方法应用于两组不相关的真实生物医学成像数据和一组合成图像。结果表明，我们的方法优于手动分割。
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