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F. Wehrmann and E. Bengtsson
ABSTRACT
In this paper, we address non-linearities in images to approach flexible templates. Templates reflect objects of a single class and are extended to have the special ability to cover the variations present about the object. An auto-associative neural network learns these variations from examples. We consider images to be related to an artificial retina where the appearance of observed objects is represented. From this point of view, non-linear grey-level changes are the consequences of global and local variations of the object. Image variation is considered in a high-dimensional image space. Thus, varying objects from the same class leave a manifold in the image space, which is modeled by the introduced network. 
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
Modelling Non-linearities in Images Using an Auto-associative Neural NetworkSite generated on Friday, 06 January 2006