Catálogo de publicaciones - libros

Fluid dynamics models the distribution of sources, sinks and vortices in imaged motion. A variety of different flow types can be obtained by specifying a key quantity known as the ratio of the Lamé moduli /. Special cases include the weakly elliptic flow /→ –2, often utilized in the Monge-Ampère transport, the Laplacian diffusion model / =–1, and the hyper-elliptic flow / → ∞ of the Stokesian dynamics. Bayesian Gaussian process generalization of the fluid displacement estimation indicates that in the absence of the specific knowledge about the ratio of the Lamé moduli, it is better to temporally balance between the rotational and divergent motion. At each time instant the Lamé moduli should minimize the difference between the fluid displacement increment and the negative gradient of the image mismatch measure while keeping the flow as incompressible as possible. An experiment presented in this paper with the interpolation of the photographed motion of Jupiter storms supports the result.

Map images are composed of semantic layers depicted in arbitrary color. Layer extraction and removal is often needed for improving readability as well as for further processing. When image is separated into the set of layers with respect to the colors, it results in appearance of severe artifacts because of the layer overlapping. In this way the extracted layers differ from the semantic data, which affects further map image processing analysis tasks. In this work, we introduce techniques for extraction and removal of the semantic layers from the map images. The techniques utilize low-complexity morphological image restoration algorithms. The restoration provides good quality of the reconstructed layers, and alleviates the affect of artifacts on the precision of image analysis tasks.

In this paper, we propose an original approach for texture and colour segmentation based on the tensor processing of the nonlinear structure tensor. While the tensor structure is a well established tool for image segmentation, its advantages were only partly used because of the vector processing of that information. In this work, we use more appropriate definitions of tensor distance grounded in concepts from information theory and compare their performance on a large number of images. We clearly show that the traditional Frobenius norm-based tensor distance is not the most appropriate one. Symmetrized KL divergence and Riemannian distance intrinsic to the manifold of the symmetric positive definite matrices are tested and compared. Adding to that, the extended structure tensor and the compact structure tensor are two new concepts that we present to incorporate gray or colour information without losing the tensor properties. The performance and the superiority of the Riemannian based approach over some recent studies are demonstrated on a large number of gray-level and colour data sets as well as real images.

We present a fast algorithm for automatic extraction of a 3D cerebrovascular system from time-of-flight (TOF) magnetic resonance angiography (MRA) data. Blood vessels are separated from background tissues (fat, bones, or grey and white brain matter) by voxel-wise classification based on precise approximation of a multi-modal empirical marginal intensity distribution of the TOF-MRA data. The approximation involves a linear combination of discrete Gaussians (LCDG) with alternating signs, and we modify the conventional Expectation-Maximization (EM) algorithm to deal with the LCDG. To validate the accuracy of our algorithm, a special 3D geometrical phantom motivated by statistical analysis of the MRA-TOF data is designed. Experiments with both the phantom and 50 real data sets confirm high accuracy of the proposed approach.

Deformable or active contour, and surface models are powerful image segmentation techniques. We introduce a novel fast and robust bi-directional parametric deformable model which is able to segment regions of intricate shape in multi-modal greyscale images. The power of the algorithm in terms of computation time and robustness is owing to the use of joint probabilities of the signals and region labels in individual points as external forces guiding the model evolution. These joint probabilities are derived from a Markov–Gibbs random field (MGRF) image model considering an image as a sample of two interrelated spatial stochastic processes. The low level process with conditionally independent and arbitrarily distributed signals relates to the observed image whereas its hidden map of regions is represented with the high level MGRF of interdependent region labels. Marginal probability distributions of signals in each region are recovered from a mixed empirical signal distribution over the whole image. In so doing, each marginal is approximated with a linear combination of Gaussians (LCG) having both positive and negative components. The LCG parameters are estimated using our previously proposed modification of the EM algorithm, and the high-level Gibbs potentials are computed analytically. Comparative experiments show that the proposed model outlines complicated boundaries of different modal objects much more accurately than other known counterparts.

The natural properties of water column usually affect underwater imagery by suppressing high-energy light. In application such as color correction of underwater images estimation of water column parameters is crucial. Diffuse attenuation coefficients are estimated and used for further processing of underwater taken data. The coefficients will give information on how fast light of different wavelengths decreases with increasing depth. Based on the exact depth measurements and data from a spectrometer the calculation of downwelling irradiance will be done. Chlorophyll concentration and a yellow substance factor contribute to a great variety of values of attenuation coefficients at different depth. By taking advantage of variations in depth, a method is presented to estimate the influence of dissolved organic matters and chlorophyll on color correction. Attenuation coefficients that depends on concentration of dissolved organic matters in water gives an indication on how well any spectral band is suited for color correction algorithm.

In this paper we present methods for removing the noise from a time sequence of digitally subtracted x-ray angiographic images. By observing the contrast agent propagation profile in a region of the angiogram one can estimate the time of arrival of that agent. Due to the large level of noise, it is difficult to detect the moment of the contrast agent arrival accurately. Hence denoising is required. Two methods are presented. The first one is based on 1D Wiener filtering of the time data. Wiener filter was chosen because it presents the optimal linear filter in the least-squares sense. The other method is based on 3D wavelet denoising via wavelet shrinkage technique, which is a nonlinear method. Since it is based on 3D wavelet basis it can perform denoising simultaneously in the spatial as well as in the time dimension of the image sequence. Wavelet based denoising proved to be superior but computationally more demanding. The experiments were performed on a sequence of cerebral angiograms.

The paper presents a new attempt to speed up fractal image encoding. The range blocks and corresponding domain blocks are categorized depending on their smoothness parameter values (smoothness estimates), introduced, from the first, to characterize manifestation of high frequency components in the image. The searching of the best matched domain block is carried out between the neighbouring (or, within the same) smoothness classes. The computational complexity of the fractal image encoding process is reduced considerably. Theoretical and experimental investigations show that extremely high compression time savings are achieved for images of size 512x512.

DCT based image compression using blocks of size 32x32 is considered. An effective method of bit-plane coding of quantized DCT coefficients is proposed. Parameters of post-filtering for removing of blocking artifacts in decoded images are given. The efficiency of the proposed method for test images compression is analyzed. It is shown that the proposed method is able to provide the quality of decoding images higher than for JPEG2000 by up to 1.9 dB.

Problem of lossy compression of vector data is considered. We attack the problem by jointly considering data reduction by polygonal approximation and quantization of the prediction errors for approximation nodes. Optimal algorithms proposed for vector data encoding with minimal distortion for given target bit-rate, and with minimal bit-rate for given maximum deviation.