Adaptive target recognition

Bir Bhanu, Yingqiang Lin, Grinnell Jones, Jing Peng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

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 languageEnglish
Title of host publicationProceedings - IEEE Workshop on Computer Vision Beyond the Visible Spectrum
Subtitle of host publicationMethods and Applications, CVBVS 1999
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages71-81
Number of pages11
ISBN (Electronic)0769500501, 9780769500508
DOIs
StatePublished - 1999
Event1st IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, CVBVS 1999 - Fort Collins, United States
Duration: 21 Jun 199922 Jun 1999

Publication series

NameProceedings - IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, CVBVS 1999

Other

Other1st IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, CVBVS 1999
Country/TerritoryUnited States
CityFort Collins
Period21/06/9922/06/99

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