The Mumford-Shah functional minimization, and related algorithms for image segmentation, involve a tradeoff between a two-dimensional image structure and one-dimensional parametric curves (contours) that surround objects or distinct regions in the image.#R##N##R##N#We propose an alternative functional that is independent of parameterization; it is a geometric functional which is given in terms of the geometry of surfaces representing the data and image in a feature space. The Γ-convergence technique is combined with the minimal surface theory in order to yield a global generalization of the Mumford-Shah segmentation functional.
['Vladimir Kluzner', 'Gershon Wolansky', 'Yehoshua Y. Zeevi']
A geometric-functional-based image segmentation and inpainting