| dc.date.accessioned | 2012-06-21T19:45:06Z | |
| dc.date.accessioned | 2018-11-26T22:26:50Z | |
| dc.date.available | 2012-06-21T19:45:06Z | |
| dc.date.available | 2018-11-26T22:26:50Z | |
| dc.date.issued | 2012 | |
| dc.identifier.citation | Machine Learning for Computer Vision (2012); eds: Cipolla R, Battiato S, Giovanni Maria F. Springer: Studies in Computational Intelligence Vol. 411. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/71199 | |
| dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/71199 | |
| dc.description.abstract | In recent years, scientific and technological advances have produced artificial systems that have matched or surpassed human capabilities in narrow domains such as face detection and optical character recognition. However, the problem of producing truly intelligent machines still remains far from being solved. In this chapter, we first describe some of these recent advances, and then review one approach to moving beyond these limited successes---the neuromorphic approach of studying and reverse-engineering the networks of neurons in the human brain (specifically, the visual system). Finally, we discuss several possible future directions in the quest for visual intelligence. | en_US |
| dc.format.extent | 15 p. | en_US |
| dc.rights | Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ | |
| dc.subject | Vision | en_US |
| dc.subject | Artificial intelligence | en_US |
| dc.title | Throwing Down the Visual Intelligence Gauntlet | en_US |
