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

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书目名称Nonlinear Eigenproblems in Image Processing and Computer Vision影响因子(影响力)




书目名称Nonlinear Eigenproblems in Image Processing and Computer Vision影响因子(影响力)学科排名




书目名称Nonlinear Eigenproblems in Image Processing and Computer Vision网络公开度




书目名称Nonlinear Eigenproblems in Image Processing and Computer Vision网络公开度学科排名




书目名称Nonlinear Eigenproblems in Image Processing and Computer Vision被引频次




书目名称Nonlinear Eigenproblems in Image Processing and Computer Vision被引频次学科排名




书目名称Nonlinear Eigenproblems in Image Processing and Computer Vision年度引用




书目名称Nonlinear Eigenproblems in Image Processing and Computer Vision年度引用学科排名




书目名称Nonlinear Eigenproblems in Image Processing and Computer Vision读者反馈




书目名称Nonlinear Eigenproblems in Image Processing and Computer Vision读者反馈学科排名

虚假

委派

原来

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

上流社会

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.

解决

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.