TEXTURED IMAGE SEGMENTATION BASED ON LOCAL SPECTRAL HISTOGRAM AND ACTIVE CONTOUR

摘要

In this paper, we propose a novel level set based active contour model to segment textured images. The proposed methods is based on the assumption that local histograms of filtering responses between foreground and background regions are statistically separable. In order to be able to handle texture non-uniformities, which often occur in real world images, we use rotation invariant filtering features and local spectral histograms as image feature to drive the snake segmentation. Automatic histogram bin size selection is carried out so that its underlying distribution can be best represented. Experimental results on both synthetic and real data show promising results and significant improvements compared to direct modeling of filtering responses.