Applied Pattern Recognition (inbunden)
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Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
246
Utgivningsdatum
2008-04-01
Upplaga
2008 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Bunke, Horst (ed.), Kandel, Abraham (ed.), Last, Mark (ed.)
Illustrationer
51 Illustrations, color; 59 Illustrations, black and white; XII, 246 p. 110 illus., 51 illus. in col
Dimensioner
234 x 156 x 16 mm
Vikt
540 g
Antal komponenter
1
Komponenter
1 Hardback
ISBN
9783540768302

Applied Pattern Recognition

av Horst Bunke, Abraham Kandel, Mark Last
Inbunden,  Engelska, 2008-04-01
1698
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A sharp increase in the computing power of modern computers, accompanied by a decrease in the data storage costs, has triggered the development of extremely powerful algorithms that can analyze complex patterns in large amounts of data within a very short period of time. Consequently, it has become possible to apply pattern recognition techniques to new tasks characterized by tight real-time requirements (e.g., person identification) and/or high complexity of raw data (e.g., clustering trajectories of mobile objects). The main goal of this book is to cover some of the latest application domains of pattern recognition while presenting novel techniques that have been developed or customized in those domains.
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Horst Bunke, Abraham Kandel, Mark Last

Innehållsförteckning

Face Recognition Applications.- Skin-based Face Detection-Extraction and Recognition of Facial Expressions.- Facial Image Processing.- Face Recognition and Pose Estimation with Parametric Linear Subspaces.- Spatio-Temporal Patterns.- 4D Segmentation of Cardiac Data Using Active Surfaces with Spatiotemporal Shape Priors.- Measuring Similarity Between Trajectories of Mobile Objects.- Graph-Based Methods.- Matching of Hypergraphs Algorithms, Applications, and Experiments.- Feature-Driven Emergence of Model Graphs for Object Recognition and Categorization.- Special Applications.- A Wavelet-based Statistical Method for Chinese Writer Identification.- Texture Analysis by Accurate Identification of a Generic MarkovGibbs Model.

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