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Textured 3D Models
Details
A vision based 3D scene analysis system is described that is capable to model complex real world scenes like old building, bridges and vestiges automatically from a sequence of calibrated images. Input to the system is a sequence of calibrated stereoscopic images which can be taken with a hand held camera. The camera is then moved throughout the scene and a long sequence of closely spaced views is recorded. A multi-view algorithm is used to link the corresponding points along a sequence of images. 3D model is reconstructed using triangulation directly from the image sequence, which allows fusing 3D surface measurements from different viewpoints into a consistent 3D model scene using a Kalman filter. The surface geometry of each scene object is approximated by a triangular surface mesh which stores the surface texture in a texture map. From the textured 3D models, realistic looking image sequences from arbitrary view points can be used in many applications. We demonstrate the successful application of the approach to several outdoor image sequences for some famous Egyptian vestiges in a framework that aims to electronically document Egypt s cultural and natural heritage.
Autorentext
Hatem received B.S. and M.Sc degrees, South Valley University, Egypt 2002 and 2007 respectively. From 2004 to 2009, he joined to Elec. Eng. Dept. Faculty of Eng. in South Valley Uni, Egypt. In 2010, he joined the IRCV Group at Rovira i Virgili Uni as PhD student. His research interests include image processing, computer vision and machine learning.
Weitere Informationen
- Allgemeine Informationen
- Sprache Englisch
- Anzahl Seiten 132
- Herausgeber LAP LAMBERT Academic Publishing
- Gewicht 215g
- Untertitel Automatic Reconstruction of 3D Models from a Sequance of Calibrated Images
- Autor Hatem Ibrahim Mahmoud Rashwan
- Titel Textured 3D Models
- Veröffentlichung 04.11.2011
- ISBN 3846545678
- Format Kartonierter Einband
- EAN 9783846545676
- Jahr 2011
- Größe H220mm x B150mm x T8mm
- Auflage Aufl.
- GTIN 09783846545676