Active contour models are widely used in image segmentation. In order to obtain ideal object boundary, researchers utilize various\ninformation to define new models for image segmentation. However, the models could not meet all scenes of image. In this paper,\nwe propose a block evolution method to improve the robustness of contour evolution. A block matrix is consisted of contours of\nformer iterations and contours of shape prior, and a nuclear norm of the matrix is a measure of the similarity of these shapes.\nThe constraint of the nuclear norm minimization is imposed on the evolution of active contour models, which could avoid large\ndeformation of the adjacent curves and keep the shape conformability of contour in the evolution.Theshape prior can be integrated\ninto the block evolution method, which is effective in dealing with missing features of images and noise.The proposed method can\nbe applied to image sequence segmentation. Experiments demonstrate that the proposed method improves the robust performance\nof active contour models and can increase the flexibility of applications in image sequence segmentation.
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