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In mathematics, the term variational analysis usually denotes the combination and extension of methods from convex optimization and the classical calculus of variations to a more general theory [5]. However, in computer vision literature, the term is frequently encountered as just a synonym for calculus of variations. This branch of mathematics deals with the minimization of functionals, which are real-valued functions on infinite-dimensional spaces, most frequently spaces of functions.
Background
In the continuous world view, images are modeled as functions on a domain \(\Omega\subset{\mathbb R}{n}\). Geometric entities like curves and surfaces are manifolds, which can be described as level sets of functions or by the characteristic functions of their interior region. Consequently, computer vision problems can often successfully be formulated as minimization problems on...
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References
Attouch H, Buttazzo G, Michaille G (2006) Variational analysis in Sobolev and BV spaces. MPS-SIAM series on optimization. Society for Industrial and Applied Mathematics, Philadelphia
Chambolle A, Caselles V, Cremers D, Novaga M, Pock T (2010) An introduction to total variation for image analysis. Radon Ser Comput Appl Math 9:263–340
Gelfand IM, Fomin SV (2003) Calculus of variations. Dover publications reprint of the 1963 edn. Dover Publications Inc., Mineola, NY
Luenberger D (1969) Optimization by vector space methods. Wiley, New York
Rockafellar RT, Wets R (2005) Variational analysis. Springer, New York
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Goldluecke, B. (2014). Variational Analysis. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_683
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DOI: https://doi.org/10.1007/978-0-387-31439-6_683
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Publisher Name: Springer, Boston, MA
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Online ISBN: 978-0-387-31439-6
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