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Record Nr. |
UNINA9910437926303321 |
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Titolo |
Machine learning for computer vision / / Roberto Cipolla, Sebastiano Battiato, and Giovanni Maria Farinella (eds.) |
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Pubbl/distr/stampa |
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Berlin ; ; New York, : Springer, c2013 |
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ISBN |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (XXII, 250 p.) |
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Collana |
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Studies in computational intelligence, , 1860-949X ; ; 411 |
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Altri autori (Persone) |
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CipollaRoberto |
BattiatoSebastiano |
FarinellaGiovanni Maria |
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Disciplina |
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Soggetti |
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Computer vision |
Machine learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Throwing Down the Visual Intelligence Gauntlet -- Actionable Information in Vision -- Learning Binary Hash Codes for Large-Scale Image Search -- Bayesian Painting by Numbers: Flexible Priors for Colour-Invariant Object Recognition -- Real-Time Human Pose Recognition in Parts from Single Depth Images -- Scale-Invariant Vote-based 3D Recognition and Registration from Point Clouds -- Multiple Classifier Boosting and Tree-Structured Classifiers -- Simultaneous detection and tracking with multiple cameras -- Applications of Computer Vision to Vehicles: an extreme test. |
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Sommario/riassunto |
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Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and |
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