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ISBN 10: 3832515887 ISBN 13: 9783832515881
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Paperback. Condizione: new. Paperback. Within the scope of this dissertation a system for appearance-based statistical classification and localization of 3-D objects in 2-D digital images is presented. The first three chapters define the object recognition task, present the mathematical background of the system, and discuss the known approaches for object recognition. Then the learning phase of the system is described. The training begins with the image acquisition, which can be done with a hand-held camera. The object poses required for the further object modeling are computed from the training image sequence with the structure-from-motion algorithm. In contrast to shape-based approaches, appearance-based methods do not use any segmentation steps to extract object features. The objects are described by 2-D local feature vectors computed directly from image pixel values using the wavelet transform. Both gray level and color images can be used for feature extraction. Finally, the object features are statistically modeled with the normal distribution and stored in the object models as density functions. Additionally, context modeling is also performed in the training phase.In the recognition phase the system classifies and localizes objects in scenes with real heterogeneous background, whereas the number of objects in a scene is unknown. First, feature vectors are calculated in the scene with the same method as in the training. Second, a maximization algorithm evaluates the learned density functions with the extracted feature vectors and yields classes and poses of objects found in the scene. Experiments made on a real data set with more than 40000 images compare the classification and localization rates for all algorithms discussed in the dissertation and show a very good performance of the system in a real world environment. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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ISBN 10: 3642230164 ISBN 13: 9783642230165
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Cameras for 3D depth imaging, using either time-of-flight (ToF) or structured light sensors, have received a lot of attention recently and have been improved considerably over the last few years. The present techniques make full-range 3D data available at video frame rates, and thus pave the way for a much broader application of 3D vision systems.A series of workshops have closely followed the developments within ToF imaging over the years. Today, depth imaging workshops can be found at every major computer vision conference. The papers presented in this volume stem from a seminar on Time-of-Flight Imaging held at Schloss Dagstuhl in October 2012. They cover all aspects of ToF depth imaging, from sensors and basic foundations, to algorithms for low level processing, to important applications that exploit depth imaging. In addition, this book contains the proceedings of a workshop on Imaging New Modalities, which was held at the German Conference on Pattern Recognition in Saarbrücken, Germany, in September 2013. A state-of-the-art report on the Kinect sensor and its applications is followed by two reports on local and global ToF motion compensation and a novel depth capture system using a plenoptic multi-lens multi-focus camera sensor.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Time-of-Flight and Depth Imaging. Sensors, Algorithms and Applications | Dagstuhl Seminar 2012 and GCPR Workshop on Imaging New Modalities | Marcin Grzegorzek (u. a.) | Taschenbuch | x | Englisch | 2013 | Springer | EAN 9783642449635 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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ISBN 10: 3832515887 ISBN 13: 9783832515881
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Within the scope of this dissertation a system for appearance-based statistical classification and localization of 3-D objects in 2-D digital images is presented. The first three chapters define the object recognition task, present the mathematical background of the system, and discuss the known approaches for object recognition. Then the learning phase of the system is described. The training begins with the image acquisition, which can be done with a hand-held camera. The object poses required for the further object modeling are computed from the training image sequence with the structure-from-motion algorithm. In contrast to shape-based approaches, appearance-based methods do not use any segmentation steps to extract object features. The objects are described by 2-D local feature vectors computed directly from image pixel values using the wavelet transform. Both gray level and color images can be used for feature extraction. Finally, the object features are statistically modeled with the normal distribution and stored in the object models as density functions. Additionally, context modeling is also performed in the training phase.In the recognition phase the system classifies and localizes objects in scenes with real heterogeneous background, whereas the number of objects in a scene is unknown. First, feature vectors are calculated in the scene with the same method as in the training. Second, a maximization algorithm evaluates the learned density functions with the extracted feature vectors and yields classes and poses of objects found in the scene. Experiments made on a real data set with more than 40000 images compare the classification and localization rates for all algorithms discussed in the dissertation and show a very good performance of the system in a real world environment. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.