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Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR & SPR 2012, Hiroshima, Japan, November 7-9, 2012, Proceedings (Paperback, 2012)
Georgy Gimel'farb, Edwin Hancock, Atsushi Imiya, Arjan Kuijper, Mineichi Kudo, …
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This volume constitutes the refereed proceedings of the Joint IAPR
International Workshops on Structural and Syntactic Pattern
Recognition (SSPR 2012) and Statistical Techniques in Pattern
Recognition (SPR 2012), held in Hiroshima, Japan, in November 2012
as a satellite event of the 21st International Conference on
Pattern Recognition, ICPR 2012. The 80 revised full papers
presented together with 1 invited paper and the Pierre Devijver
award lecture were carefully reviewed and selected from more than
120 initial submissions. The papers are organized in topical
sections on structural, syntactical, and statistical pattern
recognition, graph and tree methods, randomized methods and image
analysis, kernel methods in structural and syntactical pattern
recognition, applications of structural and syntactical pattern
recognition, clustering, learning, kernel methods in statistical
pattern recognition, kernel methods in statistical pattern
recognition, as well as applications of structural, syntactical,
and statistical methods.
Images or discrete objects, to be analyzed based on digital image data, need to be represented, analyzed, transformed, recovered etc. These problems have stimulated many interesting developments in theoretical foundations of image processing. This coherent anthology presents 27 state-of-the-art surveys and research papers on digital image geometry and topology. It is based on a winter school held at Dagstuhl Castle, Germany in December 2000 and offers topical sections on topology, representation, geometry, multigrid convergence, and shape similarity and simplification.
We met again in front of the statue of Gottfried Wilhelm von
Leibniz in the city of Leipzig. Leibniz, a famous son of Leipzig,
planned automatic logical inference using symbolic computation,
aimed to collate all human knowledge. Today, artificial
intelligence deals with large amounts of data and knowledge and
finds new information using machine learning and data mining.
Machine learning and data mining are irreplaceable subjects and
tools for the theory of pattern recognition and in applications of
pattern recognition such as bioinformatics and data retrieval. This
was the fourth edition of MLDM in Pattern Recognition which is the
main event of Technical Committee 17 of the International
Association for Pattern Recognition; it started out as a workshop
and continued as a conference in 2003. Today, there are many
international meetings which are titled "machine learning" and
"data mining," whose topics are text mining, knowledge discovery,
and applications. This meeting from the first focused on aspects of
machine learning and data mining in pattern recognition problems.
We planned to reorganize classical and well-established pattern
recognition paradigms from the viewpoints of machine learning and
data mining. Though it was a challenging program in the late 1990s,
the idea has inspired new starting points in pattern recognition
and effects in other areas such as cognitive computer vision.
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