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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wiesmann, G. Schraml, S. Litzenberger, M. Belbachir, A.N. Hofstatter, M. Bartolozzi, C. |
| Copyright Year | 2012 |
| Description | Author affiliation: Italian Institute of Technology via Morego 30, Genova Italy (Bartolozzi, C.) || Safety & Security Department, AIT Austrian Institute of Technology, Donau-City Straße 1/5, 1220 Vienna Austria (Wiesmann, G.; Schraml, S.; Litzenberger, M.; Belbachir, A.N.; Hofstatter, M.) |
| Abstract | A major challenge in robotic applications is the interaction with a dynamic environment and humans which is typically constrained by the capability of visual sensors and the computational cost of signal processing algorithms. Addressing this problem the paper presents an event-driven based embodied system for feature extraction and object recognition as a novel efficient sensory approach in robotic applications. The system is established for a mobile humanoid robot which provides the infrastructure for interfacing asynchronous vision sensors with the processing unit of the robot. By applying event-feature ”mapping” the address event representation of the sensors is enhanced by additional information that can be used for object recognition. The system is presented in the context of an exemplary application in which the robot has to detect and grasp a ball in an arbitrary state of motion. |
| Starting Page | 76 |
| Ending Page | 82 |
| File Size | 675542 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781467316118 |
| ISSN | 21607508 |
| e-ISBN | 9781467316125 |
| e-ISBN | 9781467316101 |
| DOI | 10.1109/CVPRW.2012.6238898 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-16 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Clustering algorithms Feature extraction Robot sensing systems Object recognition Voltage control |
| Content Type | Text |
| Resource Type | Article |
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