Multiscale MRF Based Image Segmentation Associate with Edge Information
结合边缘信息的多尺度MRF图像分割

Since segmented noisy image using multiscale markov random fields(MRF) still has very small error blocks or single error pixel in the smooth area,this paper proposes an approach associate edge information with multiscale MRF to reduce error classification.Because images are corrupted by the white gaussian noise,edge is difficult to detect successfully.To detect edge,an algorithm based on the stationary wavelet transform and multiplication between neighbour scales is designed. When there is a significant feature at some position,the wavelet coefficients at that position across scales have high correlation.Such correlation can be taken by multiplication between scales,so edge can be located better.Analysis and experiments demonstrate that the proposed edge detection algorithm is effective and fast.Better results are obtained using the segmentation algorithm associate with edge,most error classification among the smooth image area is corrected.The amount of added computation is only brought by the expense of the edge detection procedure.These new edge detection and segmentation algorithm are more fit for strong noisy images.