Segmenting similar shapes via weighted group-similarity active contours

This paper aims to segment similar targets shapes from multiple images by using unsupervised weighted group-similarity active contour model. We first use global contrast based saliency detector to extract the rough regions from the given multiple images group. Then a new algorithm is developed to measure the corresponding weight coefficients according to the similarities between rough regions and their latent common shape. In order to overcome the problem which caused by the trade-off between frame-specific details and group similarity more effectively during the evolution, a novel weighted group-similarity active contour model (WGSAC) is proposed, which reduces the noises generated from saliency detector dynamically and enables the curves to move toward the targets boundaries on different weighted images. Experiments on synthesized and real multiple images demonstrate that our approach is able to yield more stable segmentation results than previous methods.