Abstract
This work develops a convex optimization framework for image segmentation models, where both the unknown regions and parameters describing each region are part of the optimization process. Convex relaxations and optimization algorithms are proposed, which produce results that are independent from the initializations and closely approximate global minima. We focus especially on problems where the data fitting term depends on the mean or median image intensity within each region. We also develop a convex relaxation for the piecewise constant Mumford-Shah model, where additionally the number of regions is unknown. The approach is based on optimizing a convex energy potential over functions defined over a space of one higher dimension than the image domain.
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Bae, E., Yuan, J., Tai, XC. (2013). Simultaneous Convex Optimization of Regions and Region Parameters in Image Segmentation Models. In: Breuß, M., Bruckstein, A., Maragos, P. (eds) Innovations for Shape Analysis. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34141-0_19
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DOI: https://doi.org/10.1007/978-3-642-34141-0_19
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