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F. Solina and A. Leonardis
ABSTRACT
We propose a method for determining the proper scale for modeling visual data. An efficient architecture for selective image modeling is discussed which selects models according to the task, the nature of the scene and the computational constraints. We give an example in which models of different scales are recovered in parallel and show that this redundant representation can effectively be pruned using the criterion of Minimal Description Length. Models that are selected in the final description indicate the appropriate scale of observation. 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
Proper scale for modelling visual dataSite generated on Friday, 06 January 2006