Cai, Xiaohao and Steidl, Gabriele (2013) Multiclass segmentation by iterated ROF thresholding. In, Heyden, Anders, Kahl, Fredrik, Olsson, Carl, Oskarsson, Magnus and Tai, Xue-Cheng (eds.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8081) Energy Minimization Methods in Computer Vision and Pattern Recognition: 9th International Conference (19/08/13 - 21/08/13) Springer Berlin, Heidelberg, pp. 237-250. (doi:10.1007/978-3-642-40395-8_18).
Abstract
Variational models as the Mumford-Shah model and the active contour model have many applications in image segmentation. In this paper, we propose a new multiclass segmentation model by combining the Rudin-Osher-Fatemi model with an iterative thresholding procedure. We show that our new model for two classes is indeed equivalent to the Chan-Vese model but with an adapted regularization parameter which allows to segment classes with similar gray values. We propose an efficient algorithm and discuss its convergence under certain conditions. Experiments on cartoon, texture and medical images demonstrate that our algorithm is not only fast but provides very good segmentation results in comparison with other state-of-the-art segmentation models in particular for images containing classes of similar gray values.
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