DocumentCode :
2476925
Title :
MDL patch correspondences on unlabeled images
Author :
Karlsson, Johan ; Åström, Kalle
Author_Institution :
Centre for Math. Sci., Lund Univ., Lund, Sweden
fYear :
2008
fDate :
8-11 Dec. 2008
Firstpage :
1
Lastpage :
5
Abstract :
Automatic construction of shape and appearance models from examples via establishing correspondences across the training set has been successful in the last decades. One successful measure for establishing correspondences of high quality is minimum description length (MDL). In other approaches it has been shown that parts+geometry models which model the appearance of parts of the object and the geometric relation between the parts have been successful for automatic model building. In this paper it is shown how to fuse the above approaches and use MDL to fully automatically build optimal parts+geometry models from unlabeled images.
Keywords :
geometry; image processing; learning (artificial intelligence); MDL patch correspondences; automatic construction; automatic model building; minimum description length; shape and appearance models; unlabeled images; Data mining; Fuses; Image analysis; Image sampling; Length measurement; Mathematical model; Principal component analysis; Shape; Solid modeling; Tin;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
ISSN :
1051-4651
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
Type :
conf
DOI :
10.1109/ICPR.2008.4761191
Filename :
4761191
Link To Document :
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