Disparity energy model using a trained neuronal population

Depth information using the biological Disparity Energy Model can be obtained by using a population of complex cells. This model explicitly involves cell parameters like their spatial frequency, orientation, binocular phase and position difference. However, this is a mathematical model. Our brain do...

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Bibliographic Details
Main Authors: Martins, Jaime A., Rodrigues, J. M. F., du Buf, J. M. H.
Format: Conference Proceeding
Language:English
Subjects:
Biological information theory
biological model
Biological system modeling
Brain modeling
Correlation
disparity
Encoding
learning
population coding
Radio frequency
Training
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Summary:Depth information using the biological Disparity Energy Model can be obtained by using a population of complex cells. This model explicitly involves cell parameters like their spatial frequency, orientation, binocular phase and position difference. However, this is a mathematical model. Our brain does not have access to such parameters, it can only exploit responses. Therefore, we use a new model for encoding disparity information implicitly by employing a trained binocular neuronal population. This model allows to decode disparity information in a way similar to how our visual system could have developed this ability, during evolution, in order to accurately estimate disparity of entire scenes.
ISSN:2162-7843
DOI:10.1109/ISSPIT.2011.6151575