摘要
分水岭分割方法是一种有效的图像分割方法,但存在过分割现象,为此提出一种结合小波变换和分水岭算法的图像分割方法.该方法首先在形态学的梯度图像上,利用基于多分辨率分析的小波自适应阈值去噪算法对图像进行滤波处理,消除图像中由噪声引起的局部极小值;然后在小波重构的图像中利用形态学的极小值标定技术提取与物体相关的局部极小值,并将其标记为为原始梯度图像中的局部极小值.最后,在修改后的梯度图像上进行分水岭变换,从而得到了较好的图像分割结果.
A method for watershed image segmentation based on wavelet transform is proposed to avoid over-segmentation. First, a method of adapt threshold denoising which based on wavelet used for multi-resolution analysis is employed to smooth the original image , after Smoothing, noise which are often the causes of over-segmentation are removed. Secondly, we design a marker-extracted approach to extract the regional minima related to the object from the reconstruction of wavelet. And then extracted markers are imposed on the gradients of reconstruction as its minima, while all its intrinsic minima are suppressed. Fially, the watershed algoritnm is applied to the modified gradients by the markers to reduce effectively the over-segmentation, and achieve experimental results demonstrate the merits of this method.
出处
《小型微型计算机系统》
CSCD
北大核心
2014年第6期1382-1386,共5页
Journal of Chinese Computer Systems
关键词
小波变换
梯度图像
分水岭
标记提取
自适应阈值去噪
wavelet transform
image gradients
watershed
marker extraction
adapt threshold denoising
