From inpainting to active contours
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
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In this paper a theory is developed for variational segmentation of images using area-based segmentation functionals with non-quadratic penalty functions in the fidelity term. Two small theorems, which we believe are new to the vision community, allow us to compute the Gâteaux derivative of the considered functional, and to construct the corresponding gradient descent flow. The functional is minimized by evolving an initial curve using this gradient descent flow. If the penalty function is sub-quadratic, i.e. behaves like the pýth power of the error for p