Abstract
Target recognition is a multi-level process requiring a sequence of algorithms at low, intermediate and high levels. Generally, such systems are open loop with no feedback between levels and assuring their performance at the given probability of correct identification (PCI) and probability of false alarm (Pf) is a key challenge in computer vision and pattern recognition research. In this paper a robust closed-loop system for recognition of SAR images based on reinforcement learning is presented. The parameters in the model-based SAR target recognition are learned. The method meets performance specifications by using PCI and Pf as feedback for the learning system. It has been experimentally validated by learning the parameters of the recognition system for SAR imagery, successfully recognizing articulated targets, targets of different configuration and targets of different depression angles.
Original language | English |
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Title of host publication | Proceedings - IEEE Workshop on Computer Vision Beyond the Visible Spectrum |
Subtitle of host publication | Methods and Applications, CVBVS 1999 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 71-81 |
Number of pages | 11 |
ISBN (Electronic) | 0769500501, 9780769500508 |
DOIs | |
State | Published - 1 Jan 1999 |
Event | 1st IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, CVBVS 1999 - Fort Collins, United States Duration: 21 Jun 1999 → 22 Jun 1999 |
Other
Other | 1st IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, CVBVS 1999 |
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Country/Territory | United States |
City | Fort Collins |
Period | 21/06/99 → 22/06/99 |