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Learning illumination- and orientation-invariant representations of objects through temporal association

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84297

Wallis,  G
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84736

Backus,  BT
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84904

Langer,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Wallis, G., Backus, B., Langer, M., Huebner, G., & Bülthoff, H. (2009). Learning illumination- and orientation-invariant representations of objects through temporal association. Journal of Vision, 9(7:6), 1-8. doi:10.1167/9.7.6.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C3F1-F
Zusammenfassung
As the orientation or illumination of an object changes so does its appearance. This paper considers how observers are nonetheless able to recognize objects that have undergone such changes. In particular the paper tests the hypothesis that observers rely on temporal correlations between different object views to decide whether they are views of the same object or not. In a series of experiments subjects were shown a sequence of views representing a slowly transforming object. Testing revealed that subjects had formed object representations which were directly influenced by the temporal characteristics of the training views. In particular, introducing spurious correlations between views of different people‘s heads caused subjects to regard those views as being of a single person. This rapid and robust overriding of basic generalization processes supports the view that our recognition system tracks the correlated appearance of views of objects across time. Such view associations appear to allow the visual system to solve the view invariance problem without recourse to complex illumination models for extracting 3D form, or the use of the image plane transformations required to make appearance-based comparisons.