Structured Learning and Prediction in Computer Vision

Sebastian Nowozin

ISBN 10: 1601984561 / ISBN 13: 9781601984562

Verlag: Now Publishers Inc, 2011

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting task-specific relations and constraints.Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision. The focus is on discrete undirected graphical models which are covered in detail together with a description of algorithms for both probabilistic inference and maximum a posteriori inference. It also discusses separately recently successful techniques for prediction in general structured models.The second part of Structured Learning and Prediction in Computer Vision describes methods for parameter learning, distinguishing the classic maximum likelihood based methods from the more recent prediction-based parameter learning methods. It highlights developments to enhance current models and discusses kernelized models and latent variable models.Throughout Structured Learning and Prediction in Computer Vision the main text is interleaved with successful computer vision applications of the explained techniques. For convenience the reader can find a summary of the notation used at the end of the book.

Bestandsnummer des Verkäufers 9781601984562

Bibliografische Details

• Titel: Structured Learning and Prediction in Computer Vision
• Verlag: Now Publishers Inc
• Erscheinungsjahr: 2011
• Sprache: Englisch
• ISBN-10: 1601984561
• ISBN-13: 9781601984562
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting task-specific relations and constraints. Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision. The focus is on discrete undirected graphical models which are covered in detail together with a description of algorithms for both probabilistic inference and maximum a posteriori inference. It also discusses separately recently successful techniques for prediction in general structured models. The second part of Structured Learning and Prediction in Computer Vision describes methods for parameter learning, distinguishing the classic maximum likelihood based methods from the more recent prediction-based parameter learning methods. It highlights developments to enhance current models and discusses kernelized models and latent variable models. Throughout Structured Learning and Prediction in Computer Vision the main text is interleaved with successful computer vision applications of the explained techniques. For convenience the reader can find a summary of the notation used at the end of the book.

Über die Autorin bzw. den Autor

Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.