Adaptive target recognition

被引：0
|
作者：
Bhanu, B [1 ]
Lin, YQ [1 ]
Jones, G [1 ]
Peng, J [1 ]
机构：
[1] Univ Calif Riverside, Ctr Res Intelligent Syst, Riverside, CA 92521 USA
来源：
IEEE WORKSHOP ON COMPUTER VISION BEYOND THE VISIBLE SPECTRUM: METHODS AND APPLICATIONS (CVBVS'99) - PROCEEDINGS | 1999年
关键词：
target recognition; reinforcement learning; parameter learning;
D O I：
10.1109/CVBVS.1999.781096
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：71 / 81
页数：11
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