Parameterized Image Varieties and Estimation with Bilinear Constraints

Yakup Genc, Jean Ponce, Yoram Leedan and Peter Meer.

IEEE Conference on Computer Vision and Pattern Recognition, 1999.


Abstract

This paper addresses the problem of reliably estimating the coefficients of the parameterized image variety (PIV) [Genc and Ponce, ICCV 98] associated with the set of weak perspective images of a rigid scene, with applications in image-based rendering. Exploiting the fact that the constraints defining the PIV are linear in its coefficients and bilinear in the image data, the estimation procedure is cast in the errors-in-variables framework and solved using the method proposed in [Leedan and Meer, ICCV 98] for this type of problems. The proposed approach has been implemented, and experiments with real data are shown to yield much better prediction power than the original method based on singular value decomposition. Extensions to the more difficult case of paraperspective projection are briefly discussed.