By Nagel A., Stout E.L. (eds.)

Show description

Read Online or Download The Madison Symposium on Complex Analysis PDF

Similar analysis books

Systems Analysis and Modeling in Defense: Development, Trends, and Issues

This booklet includes the complaints of an interna­ tional symposium dedicated to Modeling and research of security strategies within the context of land/air war. It was once backed by way of Panel VII (on safeguard purposes of Operational examine) of NATO's security study team (DRG) and happened 27-29 July 1982 at NATO headquarters in Brussels.

Additional resources for The Madison Symposium on Complex Analysis

Example text

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].

Download PDF sample

Rated 4.73 of 5 – based on 42 votes