Feature-Driven Direct Non-Rigid Image Registration

In this appendix, we report an article which has been submitted to the International Journal of Computer Vision. This article has been recently accepted with minor revisions. The work presented in this article has been done in collaboration with Vincent Gay-Bellile^A.1 and Adrien Bartoli. Note that the article is reproduced without any modification compared to the journal version (except for the numbering of the sections). In particular, this implies that there might be some slight differences in the notation compared to the rest of this document.

Abstract.

The direct registration problem for images of a deforming surface has been well studied. Parametric flexible warps based, for instance, on the Free-Form Deformation or a Radial Basis Function such as the Thin-Plate Spline, are often estimated using additive Gauss-Newton-like algorithms. The recently proposed compositional framework has been shown to be more efficient, but cannot be directly applied to such non-groupwise warps.

Our main contribution in this paper is the Feature-Driven framework. It makes possible the use of compositional algorithms for most parametric warps such as those above mentioned. Two algorithms are proposed to demonstrate the relevance of our Feature-Driven framework: the Feature-Driven Inverse Compositional and the Feature-Driven Learning-based algorithms. As another contribution, a detailed derivation of the Feature-Driven warp parameterization is given for the Thin-Plate Spline and the Free-Form Deformation. We experimentally show that these two types of warps have a similar representational power. Experimental results show that our Feature-Driven registration algorithms are more efficient in terms of computational cost, without loss of accuracy, compared to existing methods.