By R. V. Gamkrelidze

The main achievements of mathematical research from Newton and Euler to trendy purposes of arithmetic in actual sciences, engineering and different components are offered during this quantity. Its 3 elements hide the equipment of research: illustration tools, asymptotic tools and rework equipment. The authors - the well known analysts M.A. Evgrafov and M.V. Fedoryuk - haven't easily provided a compendium of thoughts yet have under pressure through the underlying team spirit of a few of the equipment. the basic rules are basically offered and illustrated with fascinating and non-trivial examples. References, including courses to the literature, are supplied for these readers who desire to pass extra.

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Computer Methods and Programs in Biomedicine 27(1), 1–8 (1994) 2. : Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis, 629–639 (March 1990) 3. : Image selective smoothing and edge detection by nonlinear diffusion. Journal of Numerical Analysis 29, 845–866 (1992) 4. : Digital picture processing - an introduction. Springer, Heidelberg (1985) 5. : Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision, pp.

This paper presents an initial study which uses graphs to represent the actor’s shape and graph embedding to then convert the graph into a suitable feature vector. In this way, we can benefit from the wide range of statistical classifiers while retaining the strong representational power of graphs. The paper shows that, although the proposed method does not yet achieve accuracy comparable to that of the best existing approaches, the embedded graphs are capable of describing the deformable human shape and its evolution along the time.

Among different methods, the probabilistic graph edit distance (P-GED) proposed by Neuhaus and Bunke [10], [11] was chosen to automatically find the cost function from a labeled sample set of graphs. To this aim, the authors represented the structural similarity of two graphs by a learned probability p(g1 , g2 ) and defined the dissimilarity measure as: d(g1 , g2 ) = − log p(g1 , g2 ) (2) The main advantage of this model is that it learns the costs of edit operations automatically and is able to cope with large sets of graphs with huge distortion between samples of the same class [10], [11].

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