Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
Seller: Yes Books, Portland, ME, U.S.A.
Hardcover. Condition: Good. No Jacket. Has underling in black ink. Otherwise a clean copy in very good condition. 162 pages.
Seller: Books From California, Simi Valley, CA, U.S.A.
Hardcover. Condition: Very Good.
Seller: Fachbuch-Versandhandel, Freiburg, Germany
Condition: Gut. Hogrefe-Verlag, Hardcover, 38/2017, sehr guter Zustand, h4.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: As New. Unread book in perfect condition.
Seller: Espacio Logopédico, Barcelona, B, Spain
Tapa blanda. Condition: New. Esta guía de consulta es práctica y de tamaño manejable: contiene las tablas de la calidad formal y otras informaciones que son de utilidad para codificar las respuestas al Rorschach con el R-PAS. Es el complemento, liviano y accesible, del Manual del R-PAS. Libro.
Soft cover. Condition: Very Good. 1st Edition. Stated first edition of Paperjacks original. Likely first printing? If the dizzying collaboration between mystery authors of all conceivable styles and tastes works better as theory than practice --- MacDonald and Murphy, glib creators of Fletch and Trace, may as well be writing in a different language from romantic thriller scribe Rinehart or Road to Perdition chronicler Collins -- this rare assemblage should captivate, and the volume itself has survived a few readings with only the usual shelfwear, inevitable tanning of pages, deep creases across spine, and a price penciled on the title page, and the hints of a turned page corner midway through. Near fine condition? See photos.
Language: English
Published by Guilford Publications, 2018
ISBN 10: 1462532535 ISBN 13: 9781462532537
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New.
Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Language: English
Published by University of Illinois Press, 2008
ISBN 10: 0252075374 ISBN 13: 9780252075377
Seller: BennettBooksLtd, Los Angeles, CA, U.S.A.
paperback. Condition: New. In shrink wrap. Looks like an interesting title!
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New.
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. pp. 416.
Language: English
Published by Guilford Press 2018-01-17, 2018
ISBN 10: 1462532535 ISBN 13: 9781462532537
Seller: Chiron Media, Wallingford, United Kingdom
Hardcover. Condition: New.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Condition: New. In.
Language: English
Published by Creative Media Partners, LLC, 2025
ISBN 10: 1025133587 ISBN 13: 9781025133584
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: As New. Unread book in perfect condition.
Seller: suspiratio - online bücherstube lic.phil h.b., Basel, Switzerland
First Edition
Hardcover. Condition: Wie neu. 1. Auflage. neu - noch in folie.
Language: English
Published by Guilford Publications, 2018
ISBN 10: 1462532535 ISBN 13: 9781462532537
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
First Edition
Condition: New. 2017. 1st Edition. Hardcover. . . . . .
Language: English
Published by Creative Media Partners, LLC Mai 2025, 2025
ISBN 10: 1025133587 ISBN 13: 9781025133584
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. Neuware - Automatic target recognition (ATR) using radar commonly relies on modeling a target as a collection of point scattering centers. Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the matrix of scattering center locations augmented with the singular values of its second and third order monomial expansions. These feature sets are invariant to translation and rotation and permit the comparison of targets modeled by different numbers of scattering centers. A Mahalanobis distance metric is used that effectively identifies targets under 'real world' conditions that include noise and obscuration. In particular, eight targets of military interest are sampled in tenth-degree aspect angle increments to extract scattering centers, and 36 subclasses that encompass ten degrees are specified for each target. Each subclass is compressed to a nineteen-dimensional singular value feature set, and because the spatial distribution of the 100 nineteen-dimensional points in each subclass is approximately Gaussian, a mean and a covariance matrix represent each subclass.
Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 416 pages. 10.75x7.25x1.50 inches. In Stock.
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. pp. 416.
Hardcover. Condition: Sehr gut. Band 39 9780889375628 9780889375468 9780889374959 4 books all fine.
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. pp. 416.
Language: English
Published by Guilford Publications, 2017
ISBN 10: 1462532535 ISBN 13: 9781462532537
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Hardback. Condition: New. New copy - Usually dispatched within 4 working days.
Language: English
Published by Guilford Publications, 2017
ISBN 10: 1462532535 ISBN 13: 9781462532537
Seller: Kennys Bookstore, Olney, MD, U.S.A.
Condition: New. 2017. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.
Language: English
Published by Creative Media Partners, LLC Mai 2025, 2025
ISBN 10: 1025129830 ISBN 13: 9781025129839
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. Neuware - Automatic target recognition (ATR) using radar commonly relies on modeling a target as a collection of point scattering centers. Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the matrix of scattering center locations augmented with the singular values of its second and third order monomial expansions. These feature sets are invariant to translation and rotation and permit the comparison of targets modeled by different numbers of scattering centers. A Mahalanobis distance metric is used that effectively identifies targets under 'real world' conditions that include noise and obscuration. In particular, eight targets of military interest are sampled in tenth-degree aspect angle increments to extract scattering centers, and 36 subclasses that encompass ten degrees are specified for each target. Each subclass is compressed to a nineteen-dimensional singular value feature set, and because the spatial distribution of the 100 nineteen-dimensional points in each subclass is approximately Gaussian, a mean and a covariance matrix represent each subclass.