Titlebook: Nonlinear Eigenproblems in Image Processing and Computer Vision; Guy Gilboa Book 2018 Springer International Publishing AG, part of Spring

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書目名稱Nonlinear Eigenproblems in Image Processing and Computer Vision被引頻次




書目名稱Nonlinear Eigenproblems in Image Processing and Computer Vision被引頻次學科排名




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書目名稱Nonlinear Eigenproblems in Image Processing and Computer Vision年度引用學科排名




書目名稱Nonlinear Eigenproblems in Image Processing and Computer Vision讀者反饋




書目名稱Nonlinear Eigenproblems in Image Processing and Computer Vision讀者反饋學科排名

虛假

委派

原來

Book 2018ex functionals can induce nonlinear operators that can be analyzed within an eigenvalue framework. The text opens with an introduction to the mathematical background, together with a summary of classical variational algorithms for vision. This is followed by a focus on the foundations and applicatio

上流社會

Applications Using Nonlinear Spectral Processing,asic operations of attenuating, enhancing, and mixing certain spectral bands. Thus, a single framework with a solid theory can have very diverse applications, similar to classical linear transforms. The aim here is to give several interesting examples and to show the potential of using the nonlinear spectral formulations.

解決

Numerical Methods for Finding Eigenfunctions,n more detail a recent work by Raz Nossek and the author where a flow is used to solve the problem. This can be generalized in various ways. A generalization of Aujol et al., which is well supported theoretically, is outlined at the end of this chapter.

省略

crescendo

syring

2191-6586 singand computer vision algorithms; describes the properties of the total variation (TV) functional, and how the concept of nonlinear eigenfunctions relate to convex functionals; provides a spectral framework f978-3-030-09339-6978-3-319-75847-3Series ISSN 2191-6586 Series E-ISSN 2191-6594

FUSE

Spectral One-Homogeneous Framework,own in the finite-dimensional case. A fundamental result is that in some settings we can show a precise decomposition of the input signal into eigenfunctions. In addition, the spectral components turn to be orthogonal to each other.