摘要
为了克服分水岭算法的过分割问题,提出了一种新的带标记(marker)的分水岭分割算法。该方法首先根据邻接像素的连通性提取原始图像梯度的局部极小值点,然后采用最大熵阈值法去除由噪声及图像细节纹理所产生的伪极小值点,将修改后得到的极小值点强制作为标记,并在原始梯度图像上应用带标记的分水岭算法。该方法的优点是可以自适应地提取标记而不需要先验知识,克服了标记提取的困难。实验结果表明,该算法能有效地减少分水岭的过分割现象。
An improved marker-controlled watershed segmentation method is proposed to reduce the oversegmentation of watershed algorithm. Firstly, regional minimas in the gradient image are extracted according to the connectivity of adjacent pixels. Then, a maximum entropy threshold method is used to exclude the local minimas generated by the noise and texture. Finally, regarding the modified minimum points as markers, the watershed algorithm is applied to the modified gradients by the markers. The advantages of this method is that makers are extracted adaptively without the need for prior knowledges, which overcomes marker extraction dif- ficulties. Experimental results show that the proposed algorithm can be effective overcomes the over-segmentation problem of watershed.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第4期972-975,共4页
Systems Engineering and Electronics
基金
国家杰出基金(60525303)
燕山大学博士基金(B243)资助课题
关键词
图像分割
分水岭
多尺度梯度
标记
最大熵阈值
image segmentation
watershed
morphological gradient
marker extraction
maximum entropy threshold
