Catálogo de publicaciones - libros

In this paper, we analyze two conceptionally different approaches for shape matching: the well-known iterated closest point (ICP) algorithm and variational shape registration via level sets. For the latter, we suggest to use a numerical scheme which was introduced in the context of optic flow estimation. For the comparison, we focus on the application of shape matching in the context of pose estimation of 3-D objects by means of their silhouettes in stereo camera views. It turns out that both methods have their specific shortcomings. With the possibility of the pose estimation framework to combine correspondences from two different methods, we show that such a combination improves the stability and convergence behavior of the pose estimation algorithm.

In this paper we present a graph based approach for mining geospatial data. The system uses error-tolerant graph matching to find correspondences between the detected image information and the geospatial vector data. Spatial relations between objects are used to find a reliable object-to-object mapping. Graph matching is used as a flexible query mechanism to answer the spatial query. A condition based on the expected graph error has been presented which allows to determine the bounds of error tolerance and in this way characterizes the relevancy of a query solution. We show that the number of null labels is an important measure to determine relevancy. To be able to correctly interpret the matching results in terms of relevancy the derived bounds of error tolerance are essential.

In these note we review some basic approaches and algorithms for discrete plane/hyperplane recognition. We present, analyze, and compare related theoretical and experimental results and discuss on the possibilities for creating algorithms with higher efficiency.

Polygonal representations of digital sets with the same convexity properties allow a simple decomposition of digital boundaries into convex and concave parts. Representations whose vertices are boundary points, i.e. are integer numbers, attract most attention. The existing linear Algorithm UpPolRep computes polygonal representations with some uncorresponding parts. However, the algorithm is unable to decide if a corresponding polygonal representation still exists and in the case of existence it is unable to compute the representation. Studying situations where uncorrespondences appear we extended the algorithm. The extention does not change the time complexity. If a digital set possesses a corresponding representation then it detects this representation. Otherwise, it recognizes that such representation does not exist.

In this paper, we address the topic of monogenic curvature scale-space. Combining methods of tensor algebra, monogenic signal and quadrature filter, the monogenic curvature signal, as a novel model for intrinsically two-dimensional (i2D) structures, is derived in an algebraically extended framework. It is unified with a scale concept by employing damped spherical harmonics as basis functions. This results in a monogenic curvature scale-space. Local amplitude, phase and orientation, as independent local features, are extracted. In contrast to the Gaussian curvature scale-space, our approach has the advantage of simultaneous estimation of local phase and orientation. The main contribution is the rotationally invariant phase estimation in the scale-space, which delivers access to various phase-based applications in computer vision.

Singular points in the linear scale space provide fundamental features for the extraction of dominant parts of an image. Employing the geometrical configuration of singular points, it is possible to construct a tree in scale space. This tree expresses a hierarchical structure of dominant parts. In this paper, we clarify the graphical grammar for the construction of this tree in the linear scale space and morphological scale space. Furthermore, we show a combinatorial structure of singular points in the linear scale space and morphological scale space using conformal mapping from Euclidean space to the spherical surface.

Additive subsets have been introduced in the framework of discrete tomography with the underlying notion of x-rays. This notion can be defined from two different ways. We provide in the paper extensions of the two definitions and a proof of their equivalence in a framework where x-rays are replaced by any subsets. It results a pair of dual definitions of additivity cleared out from dispensable assumptions and a proof of their equivalence reduced to a separation theorem.

Nivat et al [3] introduced Context-free Puzzle grammars for generating connected picture arrays in the two-dimensional plane. Basic Puzzle grammars [6] constitute a subclass of these grammars. In this note we consider the Cooperating Array Grammar Systems introduced by Dassow et al [2] with Basic Puzzle grammar rules in the components instead of array grammar rules and examine the picture generating power of the resulting system, called, Cooperating Basic Puzzle Grammar System, in the maximal mode.

In this paper we present a simple method for minimal distortion development of triangulated surfaces for mapping and imaging. The method is based on classical results of F. Gehring and Y. Väisälä regarding the existence of quasi-conformal and quasi-isometric mappings between Riemannian manifolds. A random starting triangle version of the algorithm is presented. A curvature based version is also applicable. In addition the algorithm enables the user to compute the maximal distortion errors. Moreover, the algorithm makes no use to derivatives, hence it is suitable for analysis of noisy data. The algorithm is tested on data obtained from real CT images of the human brain cortex.

We study two scenarios of limited-angle binary tomography with data distorted with an unknown convolution: Either the projection data are taken from a blurred object, or the projection data themselves are blurred. These scenarios are relevant in case of scattering and due to a finite resolution of the detectors. Assuming that the unknown blurring process is adequately modeled by an isotropic Gaussian convolution kernel with unknown scale-parameter, we show that parameter estimation can be combined with the reconstruction process. To this end, a recently introduced Difference-of-Convex-Functions programming approach to limited-angle binary tomographic reconstruction is complemented with Expectation-Maximization iteration. Experimental results show that the resulting approach is able to cope with both ill-posed problems, limited-angle reconstruction and deblurring, simultaneously.