Object segmentation based on adaptive contour initialization for level set method
Ha Le, Soo-Hyung Kim, In Seop Na · 2012
In this paper, we propose a novel method for automatic object segmentation from natural scene images. This method is based on saliency map, mean shift segmentation and level set method. First, a histogram based contrast method is used to generate the saliency map of the input image. Second, the input image is segmented into clusters using mean shift. Based on the saliency map, segmented clusters are classified into background and foreground clusters. After that, an initial contour for level set method is determined by applying morphological erosion on foreground clusters. Finally, the level set method is used to evolve the initial contour and find the object of interest. The experimental results have shown that our proposed method is not only able to replace manual labeling of initial contour in level set method but also able to yield more accurate segmentation results than previous segmentation approaches.