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.
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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.
Muhammad 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 Technology and Education, Korea. His research interests include image processing, 3D shape recovery, computer vision, and machine learning.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Etat : 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. N° de réf. du vendeur 5139912
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783659210150
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783659210150
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783659210150
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