Titlebook: Computational Homology; Tomasz Kaczynski,Konstantin Mischaikow,Marian Mroz Textbook 2004 Springer Science+Business Media New York 2004 Alg

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书目名称Computational Homology影响因子(影响力)




书目名称Computational Homology影响因子(影响力)学科排名




书目名称Computational Homology网络公开度




书目名称Computational Homology网络公开度学科排名




书目名称Computational Homology被引频次




书目名称Computational Homology被引频次学科排名




书目名称Computational Homology年度引用




书目名称Computational Homology年度引用学科排名




书目名称Computational Homology读者反馈




书目名称Computational Homology读者反馈学科排名

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Cubical Homologyimensional cubes). Before that we motivated the need for a computational theory of homology using examples from image processing where it was natural to think of the images as being presented in terms of pixels (two-dimensional cubes), voxels (three-dimensional cubes), and even tetrapus (four-dimens

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Computing Homology Groupsd .(.) for some simple examples and discussed the method of elementary collapse, which can be used in special cases to compute these groups. In this chapter we want to go further and argue that the homology groups of any cubical set are computable. In fact, we will derive Algorithm 3.78, which, give

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Preview of Mapss map .: . → .. It is natural to ask if . induces a group homomorphism .: .(.) → .(.). If so, do we get useful information out of it? The answer is yes and we will spend the next three chapters explaining how to define and compute .. It is worth noting, even at this very preliminary stage, that sinc

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Obloquy

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Homology of Topological Polyhedraand 10, where we are required to work with large sets of data and for which we need a computationally effective means of computing homology. In all these examples the data itself naturally generates cubical sets. However, this cubical homology theory is unconventional, and furthermore, there is a wi