Compressão auto-adaptativa de imagens coloridas

Image compress consists in represent by small amount of data, without loss a visual quality. Data compression is important when large images are used, for example satellite image. Full color digital images typically use 24 bits to specify the color of each pixel of the Images with 8 bits for each of the primary components, red, green and blue (RGB). Compress an image with three or more bands (multispectral) is fundamental to reduce the transmission time, process time and record time. Because many applications need images, that compression image data is important: medical image, satellite image, sensor etc. In this work a new compression color images method is proposed. This method is based in measure of information of each band. This technique is called by Self-Adaptive Compression (S.A.C.) and each band of image is compressed with a different threshold, for preserve information with better result. SAC do a large compression in large redundancy bands, that is, lower information and soft compression to bands with bigger amount of information. Two image transforms are used in this technique: Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). Primary step is convert data to new bands without relationship, with PCA. Later Apply DCT in each band. Data Loss is doing when a threshold discarding any coefficients. This threshold is calculated with two elements: PCA result and a parameter user. Parameters user define a compression tax. The system produce three different thresholds, one to each band of image, that is proportional of amount information. For image reconstruction is realized DCT and PCA inverse. SAC was compared with JPEG (Joint Photographic Experts Group) standard and YIQ compression and better results are obtain, in MSE (Mean Square Root). Tests shown that SAC has better quality in hard compressions. With two advantages: (a) like is adaptive is sensible to image type, that is, presents good results to divers images kinds (synthetic, landscapes, people etc., and, (b) it need only one parameters user, that is, just letter human intervention is required

Parâmetro de regularização em problemas inversos: estudo numérico com a transformada de Radon

In general, an inverse problem corresponds to find a value of an element x in a suitable vector space, given a vector y measuring it, in some sense. When we discretize the problem, it usually boils down to solve an equation system f(x) = y, where f : U Rm ! Rn represents the step function in any domain U of the appropriate Rm. As a general rule, we arrive to an ill-posed problem. The resolution of inverse problems has been widely researched along the last decades, because many problems in science and industry consist in determining unknowns that we try to know, by observing its effects under certain indirect measures. Our general subject of this dissertation is the choice of Tykhonov´s regulaziration parameter of a poorly conditioned linear problem, as we are going to discuss on chapter 1 of this dissertation, focusing on the three most popular methods in nowadays literature of the area. Our more specific focus in this dissertation consists in the simulations reported on chapter 2, aiming to compare the performance of the three methods in the recuperation of images measured with the Radon transform, perturbed by the addition of gaussian i.i.d. noise. We choosed a difference operator as regularizer of the problem. The contribution we try to make, in this dissertation, mainly consists on the discussion of numerical simulations we execute, as is exposed in Chapter 2. We understand that the meaning of this dissertation lays much more on the questions which it raises than on saying something definitive about the subject. Partly, for beeing based on numerical experiments with no new mathematical results associated to it, partly for being about numerical experiments made with a single operator. On the other hand, we got some observations which seemed to us interesting on the simulations performed, considered the literature of the area. In special, we highlight observations we resume, at the conclusion of this work, about the different vocations of methods like GCV and L-curve and, also, about the optimal parameters tendency observed in the L-curve method of grouping themselves in a small gap, strongly correlated with the behavior of the generalized singular value decomposition curve of the involved operators, under reasonably broad regularity conditions in the images to be recovered

Modelo de risco com dependência entre os valores das indenizações e seus intervalos entre ocorrências

We present a dependent risk model to describe the surplus of an insurance portfolio, based on the article "A ruin model with dependence between claim sizes and claim intervals"(Albrecher and Boxma [1]). An exact expression for the Laplace transform of the survival function of the surplus is derived. The results obtained are illustrated by several numerical examples and the case when we ignore the dependence structure present in the model is investigated. For the phase type claim sizes, we study by the survival probability, considering this is a class of distributions computationally tractable and more general

Estimativa da profundidade do embasamento na bacia potiguar usando inversão gravimétrica

The gravity inversion method is a mathematic process that can be used to estimate the basement relief of a sedimentary basin. However, the inverse problem in potential-field methods has neither a unique nor a stable solution, so additional information (other than gravity measurements) must be supplied by the interpreter to transform this problem into a well-posed one. This dissertation presents the application of a gravity inversion method to estimate the basement relief of the onshore Potiguar Basin. The density contrast between sediments and basament is assumed to be known and constant. The proposed methodology consists of discretizing the sedimentary layer into a grid of rectangular juxtaposed prisms whose thicknesses correspond to the depth to basement which is the parameter to be estimated. To stabilize the inversion I introduce constraints in accordance with the known geologic information. The method minimizes an objective function of the model that requires not only the model to be smooth and close to the seismic-derived model, which is used as a reference model, but also to honor well-log constraints. The latter are introduced through the use of logarithmic barrier terms in the objective function. The inversion process was applied in order to simulate different phases during the exploration development of a basin. The methodology consisted in applying the gravity inversion in distinct scenarios: the first one used only gravity data and a plain reference model; the second scenario was divided in two cases, we incorporated either borehole logs information or seismic model into the process. Finally I incorporated the basement depth generated by seismic interpretation into the inversion as a reference model and imposed depth constraint from boreholes using the primal logarithmic barrier method. As a result, the estimation of the basement relief in every scenario has satisfactorily reproduced the basin framework, and the incorporation of the constraints led to improve depth basement definition. The joint use of surface gravity data, seismic imaging and borehole logging information makes the process more robust and allows an improvement in the estimate, providing a result closer to the actual basement relief. In addition, I would like to remark that the result obtained in the first scenario already has provided a very coherent basement relief when compared to the known basin framework. This is significant information, when comparing the differences in the costs and environment impact related to gravimetric and seismic surveys and also the well drillings