By Adobe Creative Team, Adobe Press
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Additional resources for Adobe(R) GoLive(R) 4.0 Classroom in a Book
When the curve coincides with the boundary of the square, both data terms are zero. 1 Curve evolution minimization of the Chan-Vese functional The Chan-Vese functional depends on two types of variables, curve γ and the regions parameters μ1 and μ2 . Its minimization can be obtained by alternating two steps, one to optimize the functional with respect to γ by curve evolution with the parameters fixed and the other to determine the optimal parameters with γ fixed. The optimal parameters can be computed analytically, and turn out to be the sample means within the regions.
Belhadj, “Polarimetric image segmentation via maximum likelihood approximation and efficient multiphase level sets,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1493–1500, 2006. 12. A. Mansouri, A. Mitiche, and C. V´azquez, “Multiregion competition: A level set extension of region competition to multiple region partioning,” Computer Vision and Image Understanding, vol. 101, no. 3, pp. 137–150, 2006. 13. A. Blake and A. Zisserman, Visual reconstruction. MIT Press, 1987.
1 The Mumford and Shah model The Mumford and Shah variational formulation is the core of variational image segmentation. It has been the subject of an impressive number of theoretical studies, for instance [1, 2, 3, 4, 5]. It bestirred intense research and influenced numerous methodological and algorithmic studies. Most of the other formulations can be explained as implementations, applications, or variants. The Mumford-Shah formulation  expresses segmentation as joint image smoothing and boundary detection.
Adobe(R) GoLive(R) 4.0 Classroom in a Book by Adobe Creative Team, Adobe Press