W. Yu, Franz Franchetti, James C. Hoe and Tsuhan Chen (Proc. IEEE International Conference on Image Processing (ICIP), 2010)
Fast and Robust Active Contours for Image Segmentation
Preprint (1.1 MB)
Published paper (link to publisher)

Active models are widely used in applications like image segmentation and tracking. Region-based active models are known for robustness to weak edges and high computational complexity. We found previous region-based models can easily get stuck in local minimums if initialization is far from the true object boundary. This is caused by an inherent ambiguity in evolution direction of the level set function when minimizing the energy. To solve this problem, we propose an intensity re-weighting (IR) model to bias the evolution process in certain direction. IR model can effectively avoid local minimums and enable much faster convergence of the evolution process. The proposed method is applied to both real and synthetic images with promising results.

Robust contours, Image segmentation