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Friday, August 7, 2020 | History

4 edition of Automatic target recognition XI found in the catalog.

Automatic target recognition XI

17-20 April 2001, Orlando, USA

  • 325 Want to read
  • 26 Currently reading

Published by SPIE in Bellingham, Wash., USA .
Written in English

    Subjects:
  • Radar -- Congresses.,
  • Optical pattern recognition -- Congresses.,
  • Image processing -- Congresses.,
  • Pattern recognition systems -- Congresses.

  • Edition Notes

    Includes bibliographical references and index.

    Other titlesAutomatic target recognition 11, Automatic target recognition eleven
    StatementFirooz A. Sadjadi, chair/editor ; sponsored ... by SPIE--the International Society for Optical Engineering.
    GenreCongresses.
    SeriesSPIE proceedings series ;, v. 4379, Proceedings of SPIE--the International Society for Optical Engineering ;, v. 4379.
    ContributionsSadjadi, Firooz A., Society of Photo-optical Instrumentation Engineers.
    Classifications
    LC ClassificationsTK6573 .A984322 2001
    The Physical Object
    Paginationxi, 586 p. :
    Number of Pages586
    ID Numbers
    Open LibraryOL3583992M
    ISBN 100819440744
    LC Control Number2002281651
    OCLC/WorldCa48431469

    A wide variety of radar-based ATR systems and applications have been developed and documented in open literature. As different as these may seem on the surface, they all tend to revolve around four basic steps, labeled in this chapter as the unified framework for ATR. First, the target set must be identified. Then the feature set must be selected, observed, and tested to identify the targets.   Abstract: It is a feasible and promising way to utilize deep neural networks to learn and extract valuable features from synthetic aperture radar (SAR) images for SAR automatic target recognition (ATR). However, it is too difficult to effectively train the deep neural networks with limited raw SAR images. In this paper, we propose a new approach to do SAR ATR, in which a multiview deep.

    Distancer, Measurements without Reflectors 11 Red Laser Pointer 13 Automatic Target Aiming (ATRplus) 15 PowerSearch PS 16 Electronic Guide Light EGL 17 Laser Plummet 18 Electromagnetic Compatibility EMC 19 FCC Statement, Applicable in U.S. 20 2 Description of the System 22 System Components recognition [6, 7], Speech recognition [8, 9] and, more recently, in face recognition [10, 11] The field of automatic target recognition (ATR) has many new challenges, but it is hoped that some of the knowledge gained from these related domains can be successfully applied to ATR. A few scientists approach to automatic target recognition.

    Feature extraction is the key technique for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP). Traditional feature extraction algorithms usually utilize shallow architectures, which result in the limited capability to characterize HRRP data and restrict the generalization performance for RATR. This two-volume set LNCS and constitutes the refereed proceedings of the 16th International Symposium on Neural Networks, ISNN , held in .


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Automatic target recognition XI Download PDF EPUB FB2

xi, pages: illustrations ; 28 cm. Series Title: Proceedings of SPIE--the International Society for Optical Engineering, v.

Other Titles: Automatic target recognition 11 Automatic target recognition eleven: Responsibility: Firooz A. Sadjadi, chair/editor ; sponsored by SPIE--the International Society for Optical Engineering.

Automatic Target Recognition Classification System Evaluation Methodology Paperback – December 4, by C. Bassham (Author) See all formats and editions Hide other formats and editions.

Price New from Used from Paperback "Please retry" $ $ — Cited by: 5. Book Description. This third edition of Automatic Target Recognition provides a roadmap for breakthrough ATR designs―with increased intelligence, performance, and autonomy. Clear distinctions are made between military problems and comparable commercial deep-learning problems.

These considerations need to be understood by ATR engineers working in the defense industry as. Books I. Maslov and I. Gertner. Evolutionary algorithms in digital image processing: A hybrid approach. LAP - Lambert Academic Publishing, I.

Maslov and I. Gertner. Multi-Sensor target recognition in image response space using evolutionary algorithms. Chapter 8 in Physics of Automatic Target Recognition, Sadjadi, Firooz.

Schachter, Bruce Jay "This third edition of Automatic Target Recognition provides a roadmap for breakthrough ATR designs with increased intelligence, performance, and autonomy.

Clear distinctions are made between military problems and comparable commercial Deep Learning problems. Contents xi. Title: Automatic Target Recognition Author: Bruce Schacter Created Date: 4/13/ PM.

"This third edition of Automatic Target Recognition provides a roadmap for breakthrough ATR designs with increased intelligence, performance, and autonomy. Clear distinctions are made between military problems and comparable commercial Deep Learning problems.

Book Description This Tutorial Text provides an inside view of the automatic target recognition (ATR) field from the perspective of an engineer working in the field for 40 years. The algorithm descriptions and testing procedures covered in the book are appropriate for addressing military problems.

Devore M and O'sullivan J () Target-Centered Models and Information-Theoretic Segmentation for Automatic Target Recognition, Multidimensional Systems and Signal Processing,(), Online publication date: 1-Jan RDI is an aircraft target recognition technique, which has not been widely explored, but has potential for contributing to automatic air target recognition.

The use of simultaneous range and frequency data has benefits in being able to localise aircraft propulsion systems along the range profile, which is not generally possible with other NCTR techniques.

A Hierarchical Classifier Using New Support Vector Machines for Automatic Target Recognition. David Casasent and Yu-Chiang Wang.

Dept. of Electrical and Computer Engineering, Carnegie Mellon University. Pittsburgh, PAUSA. [email protected], [email protected] Abstract. A binary hierarchical classifier is proposed for automatic target. DESCRIPTION This Tutorial Text provides an inside view of the automatic target recognition (ATR) field from the perspective of an engineer working in the field for 40 years.

The algorithm descriptions and testing procedures covered in the book are appropriate for addressing military problems. target. Other methods exploit optimal classifiers to determine the specific kind of target, [14]-[16].

However, in all these techniques, each target profile is presented as an input feature vector to the classifier. Since providing real-time performance in radar target recognition is a.

This second edition of Automatic Target Recognition provides an inside view of the automatic target recognition (ATR) field from the perspective of an engineer working in the field for 40 years.

The algorithm descriptions and testing procedures covered in the book. He received the B.S. degree in electrical engineering from Xidian University, Xi?an, China, inand the M.S. degree from National University of Defense Technology, Changsha, China, in He is currently pursuing the Ph.D.

degree in signal and information processing with the Science and Technology on Automatic Target Recognition. Pergamon (95)X Neural Networks, Vol. 8, No. 7/8, pp.Elsevier Science Ltd Printed in Great Britain /95 $+ SPECIAL ISSUE Neural Networks for Automatic Target Recognition STEVEN K.

ROGERS, JOHN M. COLOMBI, CURTIS E. MARTIN, JAMES C. GAINEY, KEN H. FIELDING, TOM J. BURNS, DENNIS W. RUCK, MATTHEW. Important applications are described, including optical character recognition and automatic target recognition. Software and data used in the book can be found at A useful reference for practitioners, the book is aimed at graduate students in electrical engineering, computer science and mathematics.

This Tutorial Text provides an inside view of the automatic target recognition (ATR) field from the perspective of an engineer working in the field for 40 years. The algorithm descriptions and testing procedures covered in the book are appropriate for addressing military problems.

Automatic Target Recognition (Selected SPIE Papers on CD-ROM) [Sadjadi, Firooz A.] on *FREE* shipping on qualifying offers. Automatic Target Recognition (Selected SPIE Papers on CD-ROM). Automatic Target Recognition provides an inside view of the automatic target recognition (ATR) domain from the perspective of an engineer working in the field for 40 years.

The algorithm descriptions and testing procedures covered in the book are appropriate for addressing military problems and unique aspects and considerations in the design, testing, and fielding of ATR systems. Conceptual data flow in automatic target recognition (ATR) systems.

Simple detection algorithms are applied to all the sensor data to isolate small portions that might contain targets.Automatic Target. Recognition Systems. ATR is designed to. enhance the utility of military systems by interpreting data faster and more accurately than human analysis alone.

feature. Proactive Emerging. Threat Detection (PREVENT) An analytical tool. that assesses unusual patterns or behaviors, as they happen, to detect threat activities.Part of the Lecture Notes in Computer Science book series (LNCS, volume ) Abstract.

An adaptive Chirplet filter approach is proposed to deal with the aircraft recognition problem based on high-resolution range profiles. The Chirplet filter is a joint feature extraction and target identification method derived from the feed-forward neural.