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201006 ||| eng |
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|a 9783030518660
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100 |
1 |
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|a Durou, Jean-Denis
|e [editor]
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245 |
0 |
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|a Advances in Photometric 3D-Reconstruction
|h Elektronische Ressource
|c edited by Jean-Denis Durou, Maurizio Falcone, Yvain Quéau, Silvia Tozza
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250 |
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|a 1st ed. 2020
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260 |
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|a Cham
|b Springer International Publishing
|c 2020, 2020
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300 |
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|a VII, 234 p. 112 illus., 64 illus. in color
|b online resource
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505 |
0 |
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|a 1. A Comprehensive Introduction to Photometric 3D-Reconstruction -- 2. Perspective Shape from Shading an Exposition on Recent Works with New Experiments -- 3. RGBD-Fusion: Depth Refinement for Diffuse and Specular Objects -- 4. Non-Rigid Structure from Motion and Shading -- 5. On the Well-Posedness of Uncalibrated Photometric Stereo Under General Lighting
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653 |
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|a Machine learning
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653 |
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|a Machine Learning
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653 |
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|a Computer vision
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653 |
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|a Computer Vision
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653 |
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|a Mathematical Modeling and Industrial Mathematics
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653 |
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|a Mathematical models
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700 |
1 |
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|a Falcone, Maurizio
|e [editor]
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700 |
1 |
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|a Quéau, Yvain
|e [editor]
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700 |
1 |
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|a Tozza, Silvia
|e [editor]
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041 |
0 |
7 |
|a eng
|2 ISO 639-2
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989 |
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|b Springer
|a Springer eBooks 2005-
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490 |
0 |
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|a Advances in Computer Vision and Pattern Recognition
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028 |
5 |
0 |
|a 10.1007/978-3-030-51866-0
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856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-030-51866-0?nosfx=y
|x Verlag
|3 Volltext
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082 |
0 |
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|a 006.37
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520 |
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|a This book presents the latest advances in photometric 3D reconstruction. It provides the reader with an overview of the state of the art in the field, and of the latest research into both the theoretical foundations of photometric 3D reconstruction and its practical application in several fields (including security, medicine, cultural heritage and archiving, and engineering). These techniques play a crucial role within such emerging technologies as 3D printing, since they permit the direct conversion of an image into a solid object. The book covers both theoretical analysis and real-world applications, highlighting the importance of deepening interdisciplinary skills, and as such will be of interest to both academic researchers and practitioners from the computer vision and mathematical 3D modeling communities, as well as engineers involved in 3D printing. No prior background is required beyond a general knowledge of classical computer vision models, numerical methods for optimization, and partial differential equations.
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