Abstract: Accurate symbol recognition in graphic documents needs an accurate representation of the symbols to be recognized. If structural approaches are used for recognition, symbols have to be described in terms of their shape, using structural relationships among extracted features. Unlike statistical pattern recognition, in structural methods, symbols are usually manually defined from expertise knowledge, and not automatically infered from sample images. In this work we explain one approach to learn from examples a representative structural description of a symbol, thus providing better information about shape variability. The description of a symbol is based on a probabilistic model. It consists of a set of lines described by the mean and the variance of line parameters, respectively providing information about the model of the symbol, and its shape variability. The representation of each image in the sample set as a set of lines is achieved using deformable template matching.

Abstract: We propose to use deformable template matching as a new approach to recognize characters and lineal symbols in hand-written line drawings, instead of traditional methods based on vectorization and feature extraction. Bayesian formulation of the deformable template matching allows combining fidelity to the ideal shape of the symbol with maximum flexibility to get the best fit to the input image. Lineal nature of symbols can be exploited to define a suitable representation of models and the set of deformations to be applied to them. Matching, however, is done over the original binary image to avoid losing relevant features during vectorization. We have applied this method to hand-written architectural drawings and experimental results demonstrate that symbols with high distortions from ideal shape can be accurately identified.

Abstract: Deep understanding of myocardial structure of the heart would unravel crucial knowledge for clinical and medical procedures. Although the muscular architecture of the heart has been debated by countless researchers, the controversy is still alive. Diffusion Tensor MRI, DT-MRI, is a unique imaging technique for computational validation of the muscular structure of the heart. By the complex arrangement of myocites, existing techniques can not provide comprehensive descriptions of the global muscular architecture. In this paper we introduce a multiresolution reconstruction technique based on DT-MRI streamlining for simplified global myocardial model generation. Our reconstructions can restore the most complex myocardial structures and indicate a global helical organization

Abstract: Due to its complex geometry, the basal ring is often omitted when putting different heart geometries into correspondence. In this paper, we present the first results on a new mapping of the left ventricle basal rings onto a normalized coordinate system using a fold-over free approach to the solution to the Laplacian. To guarantee correspondences between different basal rings, we imposed some internal constrained positions at anatomical landmarks in the normalized coordinate system. To prevent internal fold-overs, constraints are handled by cutting the volume into regions defined by anatomical features and mapping each piece of the volume separately. Initial results presented in this paper indicate that our method is able to handle internal constrains without introducing fold-overs and thus guarantees one-to-one mappings between different basal ring geometries.