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Aggiungi al carrelloHardcover. Condizione: Bon. Ancien livre de bibliothèque. Traces d'usure sur la couverture. Edition 1995. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Signs of wear on the cover. Edition 1995. Ammareal gives back up to 15% of this item's net price to charity organizations.
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Aggiungi al carrelloHardcover. Condizione: Très bon. Ancien livre de bibliothèque. Edition 1995. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 1995. Ammareal gives back up to 15% of this item's net price to charity organizations.
EUR 54,42
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Aggiungi al carrelloCondizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.
EUR 54,42
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EUR 54,42
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Da: medimops, Berlin, Germania
EUR 60,96
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EUR 75,00
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Aggiungi al carrelloTapa Blanda. Condizione: Bien. Colección 'Computer Science Workbench'. Tapa Blanda. Profusamente ilustrado.9784431703099. Springer. Japón. 2001. 24x16 centímetros. 323 páginas. Tapa blanda. Estado=Bien. Inglés.
EUR 153,80
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EUR 153,80
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Da: Best Price, Torrance, CA, U.S.A.
EUR 148,25
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Da: Best Price, Torrance, CA, U.S.A.
EUR 148,25
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 164,23
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EUR 164,22
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EUR 166,62
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 181,81
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EUR 179,44
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 179,23
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Da: California Books, Miami, FL, U.S.A.
EUR 193,16
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EUR 183,89
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 212,11
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Aggiungi al carrelloCondizione: New. pp. 384 3rd Edition.
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 156,74
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 157,14
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Editore: Springer London Ltd, England, 2010
ISBN 10: 1849967679 ISBN 13: 9781849967679
Lingua: Inglese
Da: Grand Eagle Retail, Mason, OH, U.S.A.
EUR 160,21
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas. Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 221,84
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Aggiungi al carrelloCondizione: New. This detailed book presents a comprehensive study on the use of Markov Random Fields for solving computer vision problems. Various vision models are presented, and this third edition includes the most recent advances with new and expanded sections. Series: Advances in Computer Vision and Pattern Recognition. Num Pages: 384 pages, 111 black & white illustrations, 11 black & white tables, biography. BIC Classification: UYA; UYQP; UYT. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 20. Weight in Grams: 587. . 2010. 3rd ed. Softcover of orig. ed. 2009. Paperback. . . . .
EUR 220,07
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware - Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.
Editore: Springer London Ltd, England, 2009
ISBN 10: 1848002785 ISBN 13: 9781848002784
Lingua: Inglese
Da: Grand Eagle Retail, Mason, OH, U.S.A.
EUR 180,42
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas. Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
EUR 275,48
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Aggiungi al carrelloCondizione: New. This detailed book presents a comprehensive study on the use of Markov Random Fields for solving computer vision problems. Various vision models are presented, and this third edition includes the most recent advances with new and expanded sections. Series: Advances in Computer Vision and Pattern Recognition. Num Pages: 384 pages, 111 black & white illustrations, 11 black & white tables, biography. BIC Classification: UYA; UYQP; UYT. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 20. Weight in Grams: 587. . 2010. 3rd ed. Softcover of orig. ed. 2009. Paperback. . . . . Books ship from the US and Ireland.
Editore: Springer London Ltd, England, 2010
ISBN 10: 1849967679 ISBN 13: 9781849967679
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 282,69
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas. Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Editore: Springer London Ltd, England, 2009
ISBN 10: 1848002785 ISBN 13: 9781848002784
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 305,77
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas. Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Da: liu xing, Nanjing, JS, Cina
EUR 62,68
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Aggiungi al carrelloSoft cover. Condizione: New. Language:Chinese.Author:LIU GUO YING DENG BIAN ZHU.Binding:Soft cover.Publisher:Science Press.