Computer vision-based approach for rite decryption in old societies

This paper presents an approach to determine the spatial arrangement of bones of horses in an excavation site and perform the 3D reconstruction of the scene. The relative 3D positioning of the bones was computed exploiting the information in images acquired at different levels, and used to relocate provided 3D models of the bones. A novel semi-supervised approach was proposed to generate dense point clouds of the bones from sparse features. The point clouds were later matched with the given models using Iterative Closest Point (ICP).

[1]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[2]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[3]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[5]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[6]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[7]  Tom Drummond,et al.  Fusing points and lines for high performance tracking , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[8]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .