A Bayesian Method for Automatic Landmark Detection in Segmented Images
A Bayesian Method for Automatic Landmark Detection in Segmented Images
复制标题
分割图像中自动地标检测的贝叶斯方法
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
Katarina Domijan
中科院分区：
文献类型：
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作者：
Katarina Domijan
图像中图形的标志点的识别在许多统计形状分析技术中起着重要作用。在某些情况下，手动地标检测是一项不切实际的任务，必须采用自动化程序来代替。标准角点检测器可用于此目的，但这种方法并不总是合适，因为最能代表图形的标志点集不一定限于角点。我们提出了一种用于自动地标检测的贝叶斯方法，其中一组 N 个地标顶点被拟合到图像分割区域的边缘。我们为给定顶点的观察分割区域提出了一个似然函数，然后使用 Metropolis 采样器对给定观察区域的地标顶点进行采样。必须仔细考虑地标分布先验的选择。
The identification of landmark points of a figure in an image plays an important role in many statistical shape analysis techniques. In certain contexts, manual landmark detection is an impractical task and an automated procedure has to be employed instead. Standard corner detectors can be used for this purpose, but this approach is not always suitable, as the set of landmark points best representing the figure is not necessarily limited to corners. We present a Bayesian approach for automatic landmark detection, where a set of N landmark vertices is fitted to the edge of a segmented region of an image. We propose a likelihood function for the observed segmented region given the vertices and then use a Metropolis sampler to sample landmark vertices given the observed region. Careful consideration has to be given to the selection of a prior for the distribution of the landmarks.