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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shukla, R. Lipasti, M. |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Wisconsin - Madison, Madison, WI, USA (Shukla, R.; Lipasti, M.) |
| Abstract | The mammalian visual system is uniquely capable of robustly recognizing objects in its field of view regardless of their orientation, scale, or position, while learning new objects from a small number of training examples and generalizing robustly to a broad class of visually similar objects. The cortical structures that implement the visual system have been successfully emulated in several biologically-inspired synthetic vision systems. Developmental evidence lends credence to the claim that visual cortical structures emerge during development, i.e. they self-organize, when exposed to training stimulus. This paper demonstrates that a set of simple developmental rules can govern the emergence of a self-learning variant of a map-seeking circuit (SL-MSC) in a simulated visual system. The SL-MSC is capable of the same visual tasks as the original hand-crafted MSC: object recognition independent of rotation, translation, and scaling, and the ability to identify and learn new objects. The SL-MSC learns invariant visual transformations by relying on temporal association in its visual field, and is able to group the transformations into independent layers. Experimental results show that the SL-MSC can generalize the rotation, translation, and scaling transformations learned for one object to new objects, leading to learning and recognition of new objects with very few training samples. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 3941626 |
| Page Count | 8 |
| File Format | |
| ISSN | 21614407 |
| e-ISBN | 9781479919604 |
| DOI | 10.1109/IJCNN.2015.7280676 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-07-12 |
| Publisher Place | Ireland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Transforms Visual systems Visualization Computational modeling Impedance matching Object recognition Image recognition |
| Content Type | Text |
| Resource Type | Article |
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