In:
Neural Computation, MIT Press, Vol. 23, No. 9 ( 2011-09), p. 2289-2323
Abstract:
Primates are very good at recognizing objects independent of viewing angle or retinal position, and they outperform existing computer vision systems by far. But invariant object recognition is only one prerequisite for successful interaction with the environment. An animal also needs to assess an object's position and relative rotational angle. We propose here a model that is able to extract object identity, position, and rotation angles. We demonstrate the model behavior on complex three-dimensional objects under translation and rotation in depth on a homogeneous background. A similar model has previously been shown to extract hippocampal spatial codes from quasi-natural videos. The framework for mathematical analysis of this earlier application carries over to the scenario of invariant object recognition. Thus, the simulation results can be explained analytically even for the complex high-dimensional data we employed.
Type of Medium:
Online Resource
ISSN:
0899-7667
,
1530-888X
DOI:
10.1162/NECO_a_00171
Language:
English
Publisher:
MIT Press
Publication Date:
2011
detail.hit.zdb_id:
1025692-1
detail.hit.zdb_id:
1498403-9
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