Structural, Syntactic, and Statistical Pattern Recognition [electronic resource] : Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings / edited by Niels Vitoria Lobo, Takis Kasparis, Fabio Roli, James T. Kwok, Michael Georgiopoulos, Georgios C. Anagnostopoulos, Marco Loog.Material type: TextLanguage: English Series: Lecture Notes in Computer Science: 5342Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008Description: online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540896890Subject(s): Computer science | Computational complexity | Artificial intelligence | Computer graphics | Computer vision | Optical pattern recognition | Computer Science | Pattern Recognition | Discrete Mathematics in Computer Science | Probability and Statistics in Computer Science | Image Processing and Computer Vision | Artificial Intelligence (incl. Robotics) | Computer GraphicsAdditional physical formats: Printed edition:: No titleDDC classification: 006.4 LOC classification: Q337.5TK7882.P3Online resources: Click here to access online
Invited Talks (Abstracts) -- SSPR -- Poster Papers -- SPR -- Poster Papers -- Invited Talks (Full Papers).
This book constitutes the refereed proceedings of the 12th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2008 and the 7th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2008, held jointly in Orlando, FL, USA, in December 2008 as a satellite event of the 19th International Conference of Pattern Recognition, ICPR 2008. The 56 revised full papers and 42 revised poster papers presented together with the abstracts of 4 invited papers were carefully reviewed and selected from 175 submissions. The papers are organized in topical sections on graph-based methods, probabilistic and stochastic structural models for PR, image and video analysis, shape analysis, kernel methods, recognition and classification, applications, ensemble methods, feature selection, density estimation and clustering, computer vision and biometrics, pattern recognition and applications, pattern recognition, as well as feature selection and clustering.