English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Talk

What is the goal of early visual processing?

MPS-Authors
/persons/resource/persons83805

Bethge,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Bethge, M. (2006). What is the goal of early visual processing?. Talk presented at Universität Osnabrück: Institute of Cognitive Science Colloquium. Osnabrück, Germany. 2006-10-25.


Cite as: https://hdl.handle.net/21.11116/0000-0004-C997-F
Abstract
Translating the information carried by the retinal image into a more useful representation is generally thought of as being an important goal of early processing in biological vision. During the last decade many models have been proposed which aim at deriving filters whose shapes resemble prominent properties of receptive fields in the early visual pathways. The filters in these models are
determined by optimizing a certain objective function. While the use of optimalityprinciples seems to imply a superior performance of these filters for scene analysis
and object recognition, it lacks thorough verification of this supposition. Minimization of the statistical higher-order dependencies between the filter outputs
(ICA) has been used to derive localized, oriented, and bandpass filters, resembling the receptive fields of simple cells in V1. A quantitative analysis of the reduction of
statistical dependencies achieved with this model, however, reveals only a small improvement in comparison with arbitrary second-order decorrelation filters. In addition, I will present psychophysical results, showing that, perceptually, the independent components of natural images exhibit even more dependencies than the non-localized basis functions of the discrete cosine transform used in image
compression. Finally, I will present a slightly different objective which similarly leads to localized, oriented, and bandpass image filters but rather seeks to divide the sensory information into different clusters of similar image content.