Get 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 publication will function a starting place for quite a few important purposes of graph concept to computing device imaginative and prescient, development reputation, and comparable components. It covers a consultant set of novel graph-theoretic equipment for advanced laptop imaginative and prescient and trend acceptance initiatives. the 1st a part of the ebook offers the applying of graph conception to low-level processing of electronic photos akin to a brand new procedure for partitioning a given photograph right into a hierarchy of homogeneous components utilizing graph pyramids, or a research of the connection among graph conception and electronic topology. half II provides graph-theoretic studying algorithms for high-level laptop imaginative and prescient and development acceptance purposes, together with a survey of graph dependent methodologies for trend acceptance and laptop imaginative and prescient, a presentation of a sequence of computationally effective algorithms for trying out graph isomorphism and comparable graph matching projects in development popularity and a brand new graph distance degree for use for fixing graph matching difficulties. ultimately, half III offers certain descriptions of a number of functions of graph-based tips on how to real-world development reputation initiatives. It encompasses a serious overview of the most graph-based and structural equipment for fingerprint category, a brand new option to visualize time sequence of graphs, and capability purposes in desktop community tracking and irregular occasion detection.

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Extra info for Applied Graph Theory in Computer Vision and Pattern Recognition

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For a branch of two pixels in width, two noisy pixels in a particular spatial position relative to each other are needed to modify the topology. More generally Multiresolution Image Segmentations in Graph Pyramids 31 to break the connectivity across an n-pixel wide branch of a region noisy pixels are needed, forming a connected path from one side of the branch to the other. This can be considered as the consequence of the sampling theorem (see [44]). All these topological modifications happen in the base of our pyramid.

For color images we run the algorithm by computing the distances (weights) in RGB color space. We choose this simple color distances in order to study the properties of the algorithm. , τ (CC) := α/|CC|, where |CC| is the size of the component CC and α is a constant. The algorithm has one running parameter α, which is used to compute the function τ . A larger constant α sets the preference for larger components. A more complex definition of τ (CC), which is large for certain shapes and small otherwise would produce a partitioning which prefers certain shapes.

Cinque, C. Guerra, and L. Levialdi. Reply: On the paper by R. Haralick. CVGIP: Image Understanding, 60(2): 250–252, 1994 50. Y. Zhang. A survey on evaluation methods for image segmentation. Pattern Recognition, 29(8): 1335–1346, 1996 51. N. E. L. A. Viergever. Validation of the interleaved pyramid for the segmentation of 3d vector images. Pattern Recognition Letters, 15(5): 469–475, 1994 Multiresolution Image Segmentations in Graph Pyramids 41 52. -M. Jolion. Stochastic pyramid revisited. Pattern Recognition Letters, 24(8): 1035– 1042, 2003 53.

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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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