Catálogo de publicaciones - libros

In the research area of historical documents it is of high interest to reconstruct the process of the emergence of a historical typesetted document. Therefore, the chronological order of the different versions of a typesetted document has to be reconstructed. This is done by manually finding differences in two versions and then deciding on the order between these two versions. In this paper we present an approach to automate the search for differences in both images. This approach uses a globally optimal image matching technique to overlay both images and colors the differences accordingly. We also present a real-world application for this approach on digitized versions of a historical book.

We consider the problem of simultaneously registering several images to a 3D model. We propose a global approach based on mutual information that extends previous methods to incorporate the color, and does not require segmentation or feature extraction. We give a stochastic model for joint optimization of multiple image-to-model alignment and we propose a heuristic to solve it. Experiments with synthetic models showed that our algorithm is robust to varying illumination and surface characteristics. Experiments with real data showed that we can achieve very good accuracy even for an object with highly specular surface, in moderate lighting conditions.

We consider the problem of non-rigid, point-to-point registration of two 3D surfaces. To avoid restrictions on the topology, we represent the surfaces as a level-set of their signed distance function. Correspondence is established by finding a displacement field that minimizes the sum of squared difference between the function values as well as their mean curvature. We use a variational formulation of the problem, which leads to a non-linear elliptic partial differential equation for the displacement field. The main contribution of this paper is the application of an adaptive finite element discretization for solving this non-linear PDE. Our code uses the software library DUNE, which in combination with pre- and post-processing through ITK leads to a powerful tool for solving this type of problem. This is confirmed by our experiments on various synthetic and medical examples. We show in this work that our numerical scheme yields accurate results using only a moderate number of elements even for complex problems.

We introduce a new approach for spline-based elastic image registration using both point landmarks and intensity information. As underlying deformation model we use Gaussian elastic body splines (GEBS), which are analytic solutions of the Navier equation under Gaus- sian forces and are represented by matrix-valued basis functions. We also incorporate landmark localization uncertainties represented by weight matrices. Our approach is formulated as an energy-minimizing functional that incorporates landmark and intensity information as well as a regularization based on GEBS. Since the approach is based on a physical deformation model, cross-effects in elastic deformations can be handled. We demonstrate the applicability of our scheme based on MR images of the human brain. It turns out that the new scheme is superior to a pure landmark-based as well as a pure intensity-based scheme.

Similarity measure is one of the main factors that affect the accuracy of intensity-based 2D/3D registration of X-ray fluoroscopy to CT images. Information theory has been used to derive similarity measure for image registration leading to the introduction of mutual information, an accurate similarity measure for multi-modal and mono-modal image registration tasks. However, it is known that the standard mutual information measure only takes intensity values into account without considering spatial information and its robustness is questionable. Previous attempt to incorporate spatial information into mutual information either requires computing the entropy of higher dimensional probability distributions, or is not robust to outliers. In this paper, we show how to incorporate spatial information into mutual information without suffering from these problems. Using a variational approximation derived from the Kullback-Leibler bound, spatial information can be effectively incorporated into mutual information via energy minimization. The resulting similarity measure has a least-squares form and can be effectively minimized by a multi-resolution Levenberg-Marquardt optimizer. Experimental results are presented on datasets of two applications: (a) intra-operative patient pose estimation from a few (e.g. 2) calibrated fluoroscopic images, and (b) post-operative cup alignment estimation from single X-ray radiograph with gonadal shielding.