摘要
在W atershed的分割图像基础上,使用贝叶斯理论的图像分割方法。首先对原始图像进行W atershed变换,然后在变换后的标注图像上进行能量的计算,通过选择最小能量的目标依次找出最理想的目标区域。设计一个先验密度来惩罚图像当中W atershed变换后相似的区域,图像分割进而变成对目标子集的最大后验估计。这样就可以逐步找出最理想目标区域和背景区域。实验结果证明,该方法有较好的分割结果。
Used Bayesian image segmentation algorithm based on Watershed transform , We calculate the energy of the label image result from the Watershed transform by designing a prior density that penalizes the area of homogeneous parts in images. The segmentation problem is the maximizing a posteriori estimation of the set of areas such we can find the optimal areas of object, and the other areas of the image are looked as background areas. The experiments indicate our Algorithm is effective for image segmentation .
出处
《计算机应用研究》
CSCD
北大核心
2006年第5期258-260,共3页
Application Research of Computers
关键词
贝叶斯框架
边缘检测
图像分割
分水线变换
Bayesian Framework
Edge Detection
Image Segmentation
Watershed Transform
