Catálogo de publicaciones - libros

Tomography deals with the reconstruction of the density distribution inside an unknown object from its projections in several directions. In Discrete tomography one focuses on the reconstruction of objects having a small, discrete set of density values. Using this prior knowledge in the reconstruction algorithm may vastly reduce the number of projections that is required to obtain high quality reconstructions. Recently the first generation of real-time tomographic scanners has appeared, capable of acquiring several images per second. Discrete tomography is well suited for real-time operation, as only few projections are required, reducing scanning time. However, for efficient real-time operation an extremely fast reconstruction algorithm is also required. In this paper we present a new reconstruction method, which is based on a feed-forward neural network. The network can compute reconstructions extremely fast, making it suitable for real-time tomography. Our experimental results demonstrate that the approach achieves good reconstruction quality.

A reliable and robust verification approach using hand-print features is presented in this paper. The characteristics of the proposed approach are that two hand-base features are employed, the palm-print and finger-print features. The system consists of two parts: a convenient device for hand-print image acquisition and an efficient algorithm for fast hand-print recognition. A robust and adaptive image coordinate system is defined to facilitate feature extraction. Discrete wavelet zero-crossing encoding scheme and 2-D Gabor filter is applied to hand-print feature extraction and representation. The experimental results demonstrate the effectiveness of the proposed system.

Since 1987 it is known that the Euclidean shortest path problem is NP-hard. However, if the 3D world is subdivided into cubes, all of the same size, defining obstacles or possible spaces to move in, then the Euclidean shortest path problem has a linear-time solution, if all spaces to move in form a simple cube-curve. The shortest path through a simple cube-curve in the orthogonal 3D grid is a minimum-length polygonal curve (MLP for short). So far only one general and linear (only with respect to measured run times) algorithm, called the rubberband algorithm , was known for an approximative calculation of an MLP. The algorithm is basically defined by moves of vertices along critical edges (i.e., edges in three cubes of the given cube-curve). A proof, that this algorithm always converges to the correct MLP, and if so, then always (provable) in linear time, was still an open problem so far (the authors had successfully treated only a very special case of simple cube-curves before). In a previous paper, the authors also showed that the original rubberband algorithm required a (minor) correction. This paper finally answers the open problem: by a further modification of the corrected rubberband algorithm, it turns into a provable linear-time algorithm for calculating the MLP of any simple cube-curve. The paper also presents an alternative provable linear-time algorithm for the same task, which is based on moving vertices within faces of cubes. For a disticntion, we call the modified original algorithm now the edge-based rubberband algorithm , and the second algorithm is the face-based rubberband algorithm ; the time complexity of both is in ${\cal O}(m)$ , where m is the number of critical edges of the given simple cube-curve.

We introduce a data processing pipeline designed to generate registration markers from range scan data. This approach uses curvature maps and histogram-templates to identify local surface features. The noise associated with real-world scans is addressed using a (common) Gauss filter and expansion-segmentation . Experimental results are presented for data from The Digital Michelangelo Project.

We present a 2D matching method based on corresponding shape outlines. By working in discrete space, our study is done by using discrete operators and avoids interpolations and approximations. To encode shapes, we polygonalize their contours and we proceed by the extraction of intrinsic properties namely length, curvature and normal vectors. We optimize then a measure of similarity controlled by weight parameters over a dynamic programming process. The approach is not sensitive to sampling errors and affine transformations. We validate our approach on simple and complex forms, we made tests also to recognize shapes. The weight parameters could be interactively modified by an end-user to customize the matching.

This paper aims to introduce a class of non-linear diffusion filterings based on deep structure analysis in scale space. In linear scale space, the trajectory of extrema is called stationary curves. This curves provides deep structure analysis and hierarchical expression of signals. The motion of extrema in linear scale space is controlled by a function of the higher derivatives of the signals. We introduce a non-linear diffusion filterings based on the absolute values of second derivative of signals.

A new particle filter, which combines genetic evolution and kernel density estimation, is proposed for moving object tracking. Particle filter (PF) solves non-linear and non-Gaussian state estimation problems in Monte Carlo simulation using importance sampling. Kernel particle filter (KPF) improves the performance of PF by using density estimation of broader kernel. However, it has the problem which is similar to the impoverishment phenomenon of PF. To deal with this problem, genetic evolution is introduced to form new filter. Genetic operators can ameliorate the diversity of particles. At the same time, genetic iteration drives particles toward their close local maximum of the posterior probability. Simulation results show the performance of the proposed approach is superior to that of PF and KPF.

We define the projection of a tiling as a matrix P = ( p _ ij ) where p _ i 1 is number of t _1,2 tiles in row i and p _ i 2 is the number of t _2,1 tiles in row i . We give an efficient algorithm to tile a 2D-square grid with only t _1,2, t _2,1, t _1,1 tiles such that the projection of this tiling is the same as the given projection.