Adaptive target recognition

Bir Bhanu; Yingqiang Lin; Grinnell Jones; Jing Peng

doi:10.1109/CVBVS.1999.781096

Adaptive target recognition

Bir Bhanu, Yingqiang Lin, Grinnell Jones, Jing Peng

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
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	https://doi.org/10.1109/CVBVS.1999.781096
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
Country/Territory	United States
City	Fort Collins
Period	21/06/99 → 22/06/99

Access to Document

10.1109/CVBVS.1999.781096

Cite this

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title = "Adaptive target recognition",

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.",

author = "Bir Bhanu and Yingqiang Lin and Grinnell Jones and Jing Peng",

year = "1999",

month = jan,

day = "1",

doi = "10.1109/CVBVS.1999.781096",

language = "English",

pages = "71--81",

booktitle = "Proceedings - IEEE Workshop on Computer Vision Beyond the Visible Spectrum",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "null ; Conference date: 21-06-1999 Through 22-06-1999",

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Bhanu, B, Lin, Y, Jones, G & Peng, J 1999, Adaptive target recognition. in Proceedings - IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, CVBVS 1999., 781096, Institute of Electrical and Electronics Engineers Inc., pp. 71-81, 1st IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, CVBVS 1999, Fort Collins, United States, 21/06/99. https://doi.org/10.1109/CVBVS.1999.781096

Adaptive target recognition. / Bhanu, Bir; Lin, Yingqiang; Jones, Grinnell et al.

Proceedings - IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, CVBVS 1999. Institute of Electrical and Electronics Engineers Inc., 1999. p. 71-81 781096.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Adaptive target recognition

AU - Bhanu, Bir

AU - Lin, Yingqiang

AU - Jones, Grinnell

AU - Peng, Jing

PY - 1999/1/1

Y1 - 1999/1/1

N2 - 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.

AB - 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.

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DO - 10.1109/CVBVS.1999.781096

M3 - Conference contribution

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EP - 81

BT - Proceedings - IEEE Workshop on Computer Vision Beyond the Visible Spectrum

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 21 June 1999 through 22 June 1999

ER -

Adaptive target recognition

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