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Download Applications of Discrete Geometry and Mathematical by Peer Stelldinger (auth.), Ullrich Köthe, Annick Montanvert, PDF

By Peer Stelldinger (auth.), Ullrich Köthe, Annick Montanvert, Pierre Soille (eds.)

This booklet constitutes the refereed complaints of the 1st Workshop on functions of Discrete Geometry and Mathematical Morphology, WADGMM 2010, held on the overseas convention on trend acceptance in Istanbul, Turkey, in August 2010. The eleven revised complete papers offered have been conscientiously reviewed and chosen from 25 submissions. The e-book used to be particularly designed to advertise interchange and collaboration among specialists in discrete geometry/mathematical morphology and capability clients of those tools from different fields of photograph research and development recognition.

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Extra resources for Applications of Discrete Geometry and Mathematical Morphology: First International Workshop, WADGMM 2010, Istanbul, Turkey, August 22, 2010, Revised Selected Papers

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Princeton University Press, New Jersey (1963) 21. : Perception-based 3D triangle mesh segmentation using fast marching watersheds. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2. IEEE Computer Society (2003) 22. : Surface parametrization and curvature measurement of arbitrary 3D objects: five practical methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 833–839 (1992) 23. : A comparison of gaussian and mean curvatures estimation methods on triangular meshes.

In Section 7, we describe how concentrated curvature can be generalized to 3-dimensional manifolds and how its restriction to the boundary surfaces defines a new mean curvature estimator. In Section 8, we present our main result that describes how concentrated curvature can be used to compute mean curvature. In Section 9, we present some experimental results to compare the different curvature estimators. Finally, Section 10 draws some concluding remarks. 2 Background Notions In this section, we briefly review some fundamental notions on curvature (see [5] for details).

6. 2 Background Definitions and Notations on Graphs The objects under study (specimens in biology, galaxies in astronomy, or pixels in image processing) are considered as the nodes of a graph. An edge is then drawn between all pairs of objects that need to be compared. The comparison often relies on a dissimilarity measure that assigns a weight to each edge. Following the notations of [10], we summarise hereafter graph definitions required in the context of clustering. , E is a subset of {{p, q} ⊆ V | p = q}.

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