A technique for synthesising novel views of an object or scene from a linear combination of basis images, originally proposed by Ullman and Basri, is briefly reviewed, extended and evaluated in a series of experiments on simple test objects. A symmetric, but overcomplete set of linear equations relating a small number of control points in the novel view to corresponding points in the basis images is used to calculate the geometry of the object as seen in the novel view. The image intensity is then calculated from a rendering model based on the distance of the novel from the basis views. Comparison of synthesised and actual images of the objects shows that the reconstructed image geometry and intensity are both accurate unless perspective effects are large. The use of an overcomplete set of linear equations to calculate the reconstructed image geometry does not lead to stability problems.
[1]
Amnon Shashua,et al.
Algebraic Functions For Recognition
,
1995,
IEEE Trans. Pattern Anal. Mach. Intell..
[2]
A. Shashua.
Geometry and Photometry in 3D Visual Recognition
,
1992
.
[3]
Bernard F. Buxton,et al.
Linear combination of face views for low bit rate face video compression
,
1998,
9th European Signal Processing Conference (EUSIPCO 1998).
[4]
Andrew Zisserman,et al.
Appendix—projective geometry for machine vision
,
1992
.
[5]
E. Rolls.
High-level vision: Object recognition and visual cognition, Shimon Ullman. MIT Press, Bradford (1996), ISBN 0 262 21013 4
,
1997
.
[6]
A. Ardeshir Goshtasby,et al.
Piecewise linear mapping functions for image registration
,
1986,
Pattern Recognit..