A hierarchical edge-stressing algorithm for adaptive image segmentation
Yannis A. Tolias, Nikos A. Kanlis, Stavros M. Panas · 2002
In this paper we present a new multiresolution edge-stressing approach for segmenting images. Our algorithm utilises the wavelet transform to obtain a multiresolution representation of the image. The low frequency residuals of each stage of the wavelet transform are being segmented using an enhanced Gibbs Random Fields model that incorporates edge information provided by the high frequency residuals. The results of the application of our algorithm are visually more attractive than the segmentation results obtained by applying both the K-means algorithm and the Adaptive Clustering Segmentation algorithm by Pappas (1992).