Read e-book online Applied Graph Theory in Computer Vision and Pattern PDF

By Walter G. Kropatsch, Yll Haxhimusa, Adrian Ion (auth.), Prof. Abraham Kandel, Prof. Dr. Horst Bunke, Dr. Mark Last (eds.)

ISBN-10: 3540680195

ISBN-13: 9783540680192

ISBN-10: 3540680209

ISBN-13: 9783540680208

This booklet will function a starting place for various valuable purposes of graph thought to desktop imaginative and prescient, development acceptance, and similar components. It covers a consultant set of novel graph-theoretic tools for complicated computing device imaginative and prescient and development acceptance initiatives. the 1st a part of the e-book offers the applying of graph thought to low-level processing of electronic pictures comparable to a brand new process for partitioning a given photograph right into a hierarchy of homogeneous parts utilizing graph pyramids, or a research of the connection among graph thought and electronic topology. half II offers graph-theoretic studying algorithms for high-level laptop imaginative and prescient and development acceptance functions, together with a survey of graph established methodologies for trend attractiveness and machine imaginative and prescient, a presentation of a chain of computationally effective algorithms for checking out graph isomorphism and similar graph matching projects in trend attractiveness and a brand new graph distance degree for use for fixing graph matching difficulties. ultimately, half III presents specific descriptions of a number of functions of graph-based how to real-world development acceptance projects. It incorporates a severe evaluate of the most graph-based and structural equipment for fingerprint category, a brand new strategy to visualize time sequence of graphs, and capability functions in machine community tracking and irregular occasion detection.

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Brun. Hierarchical image partitioning using combinatorial maps. In Proceeding of the Joint Hungarian-Austrian Conference on Image Processing and Pattern Recognition, pages 179–186, 2005 A Graphical Model Framework for Image Segmentation Rui Huang, Vladimir Pavlovic and Dimitris N. Metaxas Summary. Graphical models are probabilistic models defined in terms of graphs. The intuitive and compact graph representation and its ability to model complex probabilistic systems make graphical models a powerful modeling tool in various research areas.

The attributes of edges can be defined as the difference between features of end vertices, attre (ui , uj ) = |F (ui ) − F (uj )|, where F is some feature. , [6] attre (ui , uj ) = −||F (u )−F (u )||2 i j 2 }, where F is some feature, and σI is a parameter, which conexp{ σI trols the scale of proximity measures of F . F could be defined as F (ui ) = I(ui ), for gray value intensity images, or F (ui ) = [vi , vi · si · sin(hi ), vi · si · cos(hi )], for color images in HSV color distance [6]. However the choice of the definition of the weights and the features to be used is in general a hard problem, since the grouping cues could conflict with each other [43].

Langs. Vision pyramids that do not grow too high. Pattern Recognition Letters, 26(3): 319–337, 2005 26. G. Kropatsch and A. Montanvert. Irregular versus regular pyramid structures. In U. Eckhardt, A. Hbler, W. Nagel, and G. Werner, editors, Geometrical Problems of Image Processing, pages 11–22. Springer, Berlin Heidelberg New York, 1991 27. H. Granlund. The complexity of vision. Signal Processing, 74(1): 101–126, 1999 28. S. -W. B. L. Schwatz. Space variant image processing. International Journal of Computer Vision, 13(1): 71–90, 1994 29.

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Applied Graph Theory in Computer Vision and Pattern Recognition by Walter G. Kropatsch, Yll Haxhimusa, Adrian Ion (auth.), Prof. Abraham Kandel, Prof. Dr. Horst Bunke, Dr. Mark Last (eds.)

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