three dimensional shape recovery image von mahmood muhammad (6 Ergebnisse)

Taschenbuch. Zustand: Neu. Neuware -Inferring three-dimensional (3D) shape of real objects from visual information belongs to the main domain of the computer vision applications. Shape From Focus (SFF) is one of the passive methods that uses focus as a cue to infer the 3D structure of the object. In SFF, the objective is to find out the depth by measuring the distance of well-focused position of each object point from the camera lens. A sequence of images is acquired either by displacing the object in small steps or by changing the focal length of the lens in the camera. First, a focus measure, which is a criterion that can effectively measure the focus quality, is applied on each image pixel of the sequence. An initial depth map is obtained by maximizing the focus measure along the optical axis. In order to refine the initial depth estimate, different approximation and machine learning techniques have been used. In this book, various focus measures and SFF techniques based on machine learning approaches are discussed.Books on Demand GmbH, Überseering 33, 22297 Hamburg 120 pp. Englisch.

Taschenbuch. Zustand: Neu. Three-Dimensional Shape Recovery from Image Focus | Application of machine learning techniques in shape from focus | Muhammad Tariq Mahmood | Taschenbuch | 120 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659210150 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Inferring three-dimensional (3D) shape of real objects from visual information belongs to the main domain of the computer vision applications. Shape From Focus (SFF) is one of the passive methods that uses focus as a cue to infer the 3D structure of the object. In SFF, the objective is to find out the depth by measuring the distance of well-focused position of each object point from the camera lens. A sequence of images is acquired either by displacing the object in small steps or by changing the focal length of the lens in the camera. First, a focus measure, which is a criterion that can effectively measure the focus quality, is applied on each image pixel of the sequence. An initial depth map is obtained by maximizing the focus measure along the optical axis. In order to refine the initial depth estimate, different approximation and machine learning techniques have been used. In this book, various focus measures and SFF techniques based on machine learning approaches are discussed. 120 pp. Englisch.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mahmood Muhammad TariqMuhammad Tariq Mahmood received MS degree in intelligent software systems from BTH, Sweden in 2006 and PhD degree in mechatronics from GIST, Korea. Currently, he is assistant professor at Korea University of Tec.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Inferring three-dimensional (3D) shape of real objects from visual information belongs to the main domain of the computer vision applications. Shape From Focus (SFF) is one of the passive methods that uses focus as a cue to infer the 3D structure of the object. In SFF, the objective is to find out the depth by measuring the distance of well-focused position of each object point from the camera lens. A sequence of images is acquired either by displacing the object in small steps or by changing the focal length of the lens in the camera. First, a focus measure, which is a criterion that can effectively measure the focus quality, is applied on each image pixel of the sequence. An initial depth map is obtained by maximizing the focus measure along the optical axis. In order to refine the initial depth estimate, different approximation and machine learning techniques have been used. In this book, various focus measures and SFF techniques based on machine learning approaches are discussed.