Florent Brunet and Adrien Bartoli and Nassir Navab and Rémy Malgouyres Reinhard Koch and Andreas Kolb and Christof Rezk-Salama Pixel-Based Hyperparameter Selection for Feature-Based Image Registration International Workshop on Vision, Modeling, and Visualization (VMV) Eurographics Association 2010 November 33-40 Siegen (Germany) This paper deals with parametric image registration from point correspondences in deformable environments. In this problem, it is essential to determine correct values for hyperparameters such as the number of control points of the warp, a smoothing parameter weighting a term in the cost function, or an M-estimator threshold. This is usually carried out either manually by a trial-and-error procedure or automatically by optimizing a criterion such as the Cross-Validation score. In this paper, we propose a new criterion that makes use of all the available image photometric information. We use the point correspondences as a training set to determine the warp parameters and the photometric information as a test set to tune the hyperparameters. Our approach is fully robust in the sense that it copes with both erroneous point correspondences and outliers in the images caused by, for instance, occlusions or specularities. http://www.brnt.eu/publications/brunet2010vmvhyper.pdf http://dx.doi.org/10.2312/PE/VMV/VMV10/033-040