An improved random walk algorithm based on data-adaptive gaussian smoother for image segmentation
To improve the performance of traditional random walk algorithm, an image segmentation algorithm is proposed, which combined random walk and data-adaptive gaussian smoother. Because the medical or remote sensing images are often occupied by strong noises, a data-adaptive anisotropic filtering technique is proposed to remove noise, The filtering technique built on top of an iterative scheme that can preserve the original significant structures while suppressing the noises to the largest extent, and then compute the gradient image of the filtering image. At last the weights of edges of random walk are determined by both the gray value of original image and the salient features of data-adaptive gaussian smoother. The experimental results from synthetic as well as real images demonstrate that the proposed approach is more effective, accurate and more robust in the noise.
random walk image segmentation data-adaptive gaussian smoother gradien
Cuimei Guo Sheng Zheng Yaocheng Xie Wei Hao
Institute of Intelligent Vision and Image Information, China Three Gorges University, Yichang443002, Institute of Intelligent Vision and Image Information, China Three Gorges University, Yichang 443002
国际会议
桂林
英文
1-6
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）