The Mumford–Shah functional is a functional that is used to establish an optimality criterion for segmenting an image into sub-regions. An image is modeled as a piecewise-smooth function. The functional penalizes the distance between the model and the input image, the lack of smoothness of the model within the sub-regions, and the length of the boundaries of the sub-regions. By minimizing the functional one may compute the best image segmentation. The functional was proposed by mathematicians David Mumford and Jayant Shah in 1989.[1]
Definition of the Mumford–Shah functional[edit]
Consider an image I with a domain of definition D, call J the image's model, and call B the boundaries that are associated with the model: the Mumford–Shah functional E[ J,B ] is defined as
![{\displaystyle E[J,B]=\alpha \int _{D}(I({\vec {x}})-J({\vec {x}}))^{2}\,\mathrm {d} {\vec {x}}+\beta \int _{D/B}{\vec {\nabla }}J({\vec {x}})\cdot {\vec {\nabla }}J({\vec {x}})\,\mathrm {d} {\vec {x}}+\gamma \int _{B}ds}](https://wikimedia.org/api/rest_v1/media/math/render/svg/64ae981b6dd1aae26d3d090561295686e26e9eca)
Optimization of the functional may be achieved by approximating it with another functional, as proposed by Ambrosio and Tortorelli.[2]
Minimization of the functional[edit]
Ambrosio–Tortorelli limit[edit]
Ambrosio and Tortorelli[2] showed that Mumford–Shah functional E[ J,B ] can be obtained as the limit of a family of energy functionals E[ J,z,ε ] where the boundary B is replaced by continuous function z whose magnitude indicates the presence of a boundary. Their analysis show that the Mumford–Shah functional has a well-defined minimum. It also yields an algorithm for estimating the minimum.
The functionals they define have the following form:
![{\displaystyle E[J,z;\varepsilon ]=\alpha \int (I({\vec {x}})-J({\vec {x}}))^{2}\,\mathrm {d} {\vec {x}}+\beta \int z({\vec {x}})|{\vec {\nabla }}J({\vec {x}})|^{2}\,\mathrm {d} {\vec {x}}+\gamma \int \{\varepsilon |{\vec {\nabla }}z({\vec {x}})|^{2}+\varepsilon ^{-1}\phi ^{2}(z({\vec {x}}))\}\,\mathrm {d} {\vec {x}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/1cf4473c4123051a30a4f05102602d5cfe141c8e)
where ε > 0 is a (small) parameter and ϕ(z) is a potential function. Two typical choices for ϕ(z) are
- This choice associates the edge set B with the set of points z such that ϕ1(z) ≈ 0
- This choice associates the edge set B with the set of points z such that ϕ2(z) ≈ 1/4
![{\displaystyle \phi _{1}(z)=(1-z^{2})/2\quad z\in [-1,1].}](https://wikimedia.org/api/rest_v1/media/math/render/svg/79dbc75262a8bd74f3266d433750c36f81871dc6)
![{\displaystyle \phi _{2}(z)=z(1-z)\quad z\in [0,1].}](https://wikimedia.org/api/rest_v1/media/math/render/svg/d24eb3393043b1724e5808ebc4e062aa6936b511)
The non-trivial step in their deduction is the proof that, as , the last two terms of the energy function (i.e. the last integral term of the energy functional) converge to the edge set integral ∫Bds.
The energy functional E[ J,z,ε ] can be minimized by gradient descent methods, assuring the convergence to a local minimum.
Ambrosio, Fusco, and Hutchinson, established a result to give an optimal estimate of the Hausdorff dimension of the singular set of minimizers of the Mumford-Shah energy.[3]
Minimization by splitting into one-dimensional problems[edit]
The Mumford-Shah functional can be split into coupled one-dimensional subproblems. The subproblems are solved exactly by dynamic programming. [4]
See also[edit]
Notes[edit]
References[edit]
- Camillo, De Lellis; Focardi, Matteo; Ruffini, Berardo (October 2013), "A note on the Hausdorff dimension of the singular set for minimizers of the Mumford–Shah energy", Advances in Calculus of Variations, 7 (4): 539–545, arXiv:1403.3388, doi:10.1515/acv-2013-0107, ISSN 1864-8258, S2CID 2040612, Zbl 1304.49091
- Ambrosio, Luigi; Fusco, Nicola; Hutchinson, John E. (2003), "Higher integrability of the gradient and dimension of the singular set for minimisers of the Mumford-Shah functional", Calculus of Variations and Partial Differential Equations, 16 (2): 187–215, doi:10.1007/s005260100148, S2CID 55078333, Zbl 1047.49015
- Ambrosio, Luigi; Tortorelli, Vincenzo Maria (1990), "Approximation of functionals depending on jumps by elliptic functionals via Γ-convergence", Communications on Pure and Applied Mathematics, 43 (8): 999–1036, doi:10.1002/cpa.3160430805, MR 1075076, Zbl 0722.49020
- Ambrosio, Luigi; Fusco, Nicola; Pallara, Diego (2000). Functions of bounded variation and free discontinuity problems. Oxford Mathematical Monographs. New York: The Clarendon Press, Oxford University Press. pp. 434. ISBN 9780198502456. Zbl 0957.49001.
- Mumford, David; Shah, Jayant (1989), "Optimal Approximations by Piecewise Smooth Functions and Associated Variational Problems" (PDF), Communications on Pure and Applied Mathematics, XLII (5): 577–685, doi:10.1002/cpa.3160420503, MR 0997568, Zbl 0691.49036
- Hohm, Kilian; Storath, Martin; Weinmann, Andreas (2015), "An algorithmic framework for Mumford–Shah regularization of inverse problems in imaging" (PDF), Inverse Problems, 31 (11): 115011, Bibcode:2015InvPr..31k5011H, doi:10.1088/0266-5611/31/11/115011, S2CID 15365352