Abstract
We merge techniques developed in the Beltrami framework to deal with multi-channel, i.e. color images, and the Mumford-Shah func- tional for segmentation. The result is a color image enhancement and segmentation algorithm. The generalization of the Mumford-Shah idea includes a higher dimension and codimension and a novel smoothing mea- sure for the color components and for the segmenting function which is introduced via the I-convergence approach. We use the I-convergence technique to derive, through the gradient descent method, a system of coupled PDEs for the color coordinates and for the segmenting function.
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Kimmel, R., Sochen, N.A. (1999). Geometric-Variational Approach for Color Image Enhancement and Segmentation. In: Nielsen, M., Johansen, P., Olsen, O.F., Weickert, J. (eds) Scale-Space Theories in Computer Vision. Scale-Space 1999. Lecture Notes in Computer Science, vol 1682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48236-9_26
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DOI: https://doi.org/10.1007/3-540-48236-9_26
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