Wang xiaosong (44 résultats)

AK. Lübecker Museen, 8.7.2012 - 2.9.2012 / Ludwig Museum Koblenz,4.11.2012 - 16.1.2013. Mit Texten von Thorsten Rodiek, Beate Reiffenscheid, Demetrio Paparoni, Gao Minglu und Fan D'ian. München: Hirmer Verlag, 2012. Rot illustr. OHardc. 246 Seiten mit Texten in deutsch, englisch und chinesisch sowie 64 teils doppelseitige, farbige Werkabbildungen und Textillustrationen, Bio- und Bibliographie des Künstlers. - 32,5 x 25,5. * Wang Xiaosong ist ein 1964 in Wuhan geborener chinesischer Künstler und Kunst-Professor. Wang, der von 1983 bis 1997 in Peking und Berlin studiert hat, verwendet in seinen Arbeiten einen Zusammenfluss chinesischer und westlicher Kunst. Er nahm 2011 an einem 'Gegenprogramm' zum offiziellen Beitrag der Biennale teilnahm und sorgte so für Aufsehen mit seinen Gemälden. - Druckfrisch und unbenutzt. - Versand gewichtsbedingt nur mit Paket-Zusatzporto möglich .…

Hardcover. Etat : Sehr gut. 123 Ein visuell beeindruckendes Buch über die Werke von Wang Xiaosong und Xiao Hui Wang, das die kulturellen Kontraste zwischen Ost und West thematisiert. Zustand: Einband mit geringfügigen Gebrauchsspuren, insgesamt SEHR GUTER Zustand! HC1-498-1/8-00728999 Sprache: Englisch Gewicht in Gramm: 4437. …

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Etat : New.

Hardback. Etat : New. 2019 ed. This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.…

Etat : New.

Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Etat : New.

Etat : New.

Hardback. Etat : New. 2019 ed. This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.…

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Taschenbuch. Etat : Neu. Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics | Le Lu (u. a.) | Taschenbuch | Advances in Computer Vision and Pattern Recognition | xi | Englisch | 2020 | Springer | EAN 9783030139711 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Etat : New.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory.The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.…

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory.The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.…

Etat : New. pp. XI, 461 177 illus., 156 illus. in color. 1st ed. 2019 edition NO-PA16APR2015-KAP.

Etat : New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Etat : New.

Paperback. Etat : New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Hardcover. Etat : Brand New. 476 pages. 9.25x6.10x1.34 inches. In Stock.

Hardcover. Etat : Brand New. 520 pages. 8.66x6.08x0.89 inches. In Stock.

Etat : New.

Etat : New.

Hardcover. Etat : New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Original-Heft; 4°; 28 unpaginierte Seiten; mit Abbildungen; 1 Beilage. Fünf Künstler haben jeweils ihre Biogramme signiert (Wang Xiaosong, Shan Zeng, Chao Ping, Zhang Quikai und Ma Yong). Sehr gutes Exemplar. Sprache: Deutsch 200 gr.

Pp. Etat : Wie neu. 1.Auflage. 2 Bände im Schuber, zus. 472 S., überw. Ill. Neuwertiges Exemplar, Originalverschweißt. - Beitr. teilw. dt., teilw. engl., teilw. chin. - Abstrakte Kunst nimmt in China einen Sonderstatus ein. Erst in den vergangenen Jahren behauptet sie sich allmählich gegenüber der figurativen Malerei. Wan Xiaosong war einer der Künstler, der 2011 an einem 'Gegenprogramm' zum offiziellen Beitrag der Biennale teilnahm und so für Aufsehen mit seinen Gemälden sorgte. Die Fotokünstlerin Xiao Hui Wang gilt inzwischen weltweit als eine der einflussreichsten Künstlerinnen unserer Zeit. In ihren neueren Arbeiten wendet sie sich fotografisch scheinbar abstrakten Bildern zu, die sie am Elektronenrastermikroskop überarbeitet. Sie öffnet eine Tür zu einer wunderbaren Welt, die wir nie gesehen haben, und begeht ein neues Gebiet des künstlerischen Schaffens. ISBN 9783777459912 Sprache: Deutsch Gewicht in Gramm: 4424.…

Hardcover. Etat : Sehr gut. 472 Seiten Dieser Katalog begleitet die Einzelausstellung von Wang Xiaosong in den Lübecker Museen und dem LUDWIGMUSEUM, mit einem Fokus auf introspektive und moderne Kunstwerke. Zustand: Einband mit geringfügigen Gebrauchsspuren, insgesamt SEHR GUTER Zustand! HC1-470-3/8-00709831 Sprache: Deutsch Gewicht in Gramm: 4424. …