Seismic transitions of the subsurface are typically considered as zero-order singularities (step functions). According to this model, the conventional deconvolution problem aims at recovering the seismic reflectivity as a sparse spike train. However, recent multiscale analysis on sedimentary records revealed the existence of accumulations of varying order singularities in the subsurface, which give rise to fractional-order discontinuities. This observation not only calls for a richer class of seismic reflection waveforms, but it also requires a different methodology to detect and characterize these reflection events. For instance, the assumptions underlying conventional deconvolution no longer hold. Because of the bandwidth limitation of seismic data, multiscale analysis methods based on the decay rate of wavelet coefficients may yield ambiguous results. We avoid this problem by formulating the estimation of the singularity orders by a parametric nonlinear inversion method.

This dissertation analyzes the complex relationships between language, immigration, and labor and housing market outcomes. First, I model the urban labor market as segmented by language barriers. The prediction of this segmentation theory is confirmed by Canadian Census data, which allow me to identify a worker's labor market segment by her work language. Second, I explore whether the housing market reflects people's willingness to pay for higher quality social-ethnic interactions. By combining housing transaction data and Census information, I am able to test such a relationship with positive results. Finally, I ask what properties housing price series have if some people have better knowledge of the future immigration/migration flows to a city. Under this setup, the price series become serially correlated and the price volatility varies over time. The model also explains the long-standing price-volume relationship in housing transaction data.

The accurate detection of static object boundaries such as contours or surfaces and dynamic tunnels of moving objects via deformable models is an ongoing research topic in computer vision. Most deformable models attempt to converge towards a desired solution by minimizing the sum of internal (prior) and external (measurement) energy terms. Such an approach is elegant, but frequently mis-converges in the presence of noise or complex boundaries and typically requires careful semi-dependent parameter tuning and initialization. Furthermore, current deformable model based approaches are computationally demanding which precludes real-time use. To address these limitations, a decoupled deformable model (DDM) is developed which optimizes the two energy terms separately. Essentially, the DDM consists of a measurement update step, employing a Hidden Markov Model (HMM) and Maximum Likelihood (ML) estimator, followed by a separate prior step, which modifies the updated deformable model based on the relative strengths of the measurement uncertainty and the non-stationary prior. The non-stationary prior is generated by using a curvature guided importance sampling method to capture high curvature regions. By separating the measurement and prior steps, the algorithm is less likely to mis-converge; furthermore, the use of a non-iterative ML estimator allows the method to converge more rapidly than energy-based iterative solvers. The full functionality of the DDM is developed in three phases. First, a DDM in 2D called the decoupled active contour (DAC) is developed to accurately identify the boundary of a 2D object in the presence of noise and background clutter. To carry out this task, the DAC employs the Viterbi algorithm as a truncated ML estimator, curvature guided importance sampling as a non-stationary prior generator, and a linear Bayesian estimator to fuse the non-stationary prior with the measurements. Experimental results clearly demonstrate that the DAC is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to three other published methods and across many images, the DAC is found to be faster and to offer consistently accurate boundary identification. Second, a fast decoupled active contour (FDAC) is proposed to accelerate the convergence rate and the scalability of the DAC without sacrificing the accuracy by employing computationally efficient and scalable techniques to solve the three primary steps of DAC. The computational advantage of the FDAC is demonstrated both experimentally and analytically compared to three computationally efficient methods using illustrative examples. Finally, an extension of the FDAC from 2D to 3D called a decoupled active surface (DAS) is developed to precisely identify the surface of a volumetric 3D image and the tunnel of a moving 2D object. To achieve the objectives of the DAS, the concepts of the FDAC are extended to 3D by using a specialized 3D deformable model representation scheme and a computationally and storage efficient estimation scheme. The performance of the DAS is demonstrated using several natural and synthetic volumetric images and a sequence of moving objects.

Verslo mokymų rinka išgyvena dinamišką laikotarpį, kuris pasižymi mokymų apimčių didėjimu, naujų įmonių teikiančių tokias paslaugas atsiradimu. Augimo laikotarpiu daugelis tradicinių verslo problemų nėra aštrios, tačiau rinkai pasiekus brandos stadiją, situacija keičiasi. Kainodaros sprendimai – vieni sudėtingiausių ir turinčių didelį poveikį verslo stabilumui bei plėtrai. Šio darbo tikslas – padėti mokymus teikiančioms bendrovėms tvirčiau jaustis ieškant atsakymų į klausimus – kokie yra klientų segmentai pagal jautrumą kainai, radus šiuos atsakymus, didesnė tikimybė priimti teisingus plėtros sprendimus. Baigiamojo darbo tema: Lietuvos verslo mokymų rinkos segmentavimo galimybės taikant jautrumo kainai kriterijų. Darbo tikslas - nustatyti, kokios yra mokymų rinkos segmentavimo galimybės pagal jautrumą mokymo kainai. Darbo uždaviniai: 1. Išanalizuoti Lietuvos mokymo paslaugų rinką. 2. Surinkti duomenis apie šiuo metu Lietuvos mokymo paslaugų rinkoje taikomas kainas. 3. Atlikti kiekybinę apklausą, nustatant kainos jautrumo parametrus skirtingiems Lietuvos mokymo paslaugų klientams. 4. Pasiūlyti prioritetus, kaip Lietuvos mokymų rinka gali būti segmentuojama pagal jautrumą mokymo kainai ir kokie kainodaros metodai taikytini skirtingiems segmentams. Tyrimo metodika. Darbe buvo panaudotas kiekybinis tyrimo metodas - Westendorp „Price Sensitivity Meter“ („Kainos jautrumo nustatymo“) klausimynas. / The market of business training experiences dynamic time, which is characterized by an increasing scope of training and discovery of new enterprises providing such services. At the stage of development a lot of traditional business problems are not severe, however, when the market reaches the maturity stage the situation changes. Pricing solutions are one of the most complex and having a huge impact on business stability and expansion. The aim of the research is to help training providers to feel more confident while looking for an answer to the question – what are the client segments according to the price sensitivity. After having answered it, there is greater likelihood to take the right decisions in expansion. Theme: Application of Price Sensitivity Criterion in the Market of Business Training in Lithuania and its Segmentation Position Objectives: to determine the segmentation position of executive training market in applying the price sensitivity criterion. Aims of the research: 1. To analyze the market in the Training service sector of Lithuania. 2. To carry out analysis in price rates in the training sector in Lithuania. 3. To conduct a survey in order to set rice sensitivity parameters on different Lithuanian clients in the training service sector. To propose a method of how the training sector in Lithuania can be segmented according to the price sensitivity in training and what pricing methods could be applied to different segments... [to full text]

Melanoma is the deadliest form of skin cancer if left untreated. Incidence rates of melanoma have been increasing, especially among young adults, but survival rates are high if detected early. Unfortunately, the time and costs required for dermatologists to screen all patients for melanoma are prohibitively expensive. There is a need for an automated system to assess a patient's risk of melanoma using photographs of their skin lesions. Dermatologists could use the system to aid their diagnosis without the need for special or expensive equipment. One challenge in implementing such a system is locating the skin lesion in the digital image. Most existing skin lesion segmentation algorithms are designed for images taken using a special instrument called the dermatoscope. The presence of illumination variation in digital images such as shadows complicates the task of finding the lesion. The goal of this research is to develop a framework to automatically correct and segment the skin lesion from an input photograph. The first part of the research is to model illumination variation using a proposed multi-stage illumination modeling algorithm and then using that model to correct the original photograph. Second, a set of representative texture distributions are learned from the corrected photograph and a texture distinctiveness metric is calculated for each distribution. Finally, a texture-based segmentation algorithm classifies regions in the photograph as normal skin or lesion based on the occurrence of representative texture distributions. The resulting segmentation can be used as an input to separate feature extraction and melanoma classification algorithms. The proposed segmentation framework is tested by comparing lesion segmentation results and melanoma classification results to results using other state-of-the-art algorithms. The proposed framework has better segmentation accuracy compared to all other tested algorithms. The segmentation results produced by the tested algorithms are used to train an existing classification algorithm to identify lesions as melanoma or non-melanoma. Using the proposed framework produces the highest classification accuracy and is tied for the highest sensitivity and specificity.

In response to shifting borders and radical changes in political and economic regimes, a great number of Hungarian Romanians left their homeland in the last century. Drawing on ethnographic fieldwork in a Hungarian village in Romania, in this thesis I argue that the growing uncertainty in villagers’ working lives, a result of the high unemployment accompanying post-socialist transformation, and ethnic and class based disadvantage in Romania, impels them to engage in pluriactivity in their livelihood strategies. This includes circular labour migration in Hungary and other European Union states. Economic inequalities within the expanded EU create an ethnically segmented labour market, in which working class Transylvanian Hungarians become associated with certain types of work, in this case, temporary and often undocumented jobs in the least desirable sectors of the economy.

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The objective of this study is to apply digital image processing (DIP) techniques to optical coherence tomography (OCT) images and develop computer-based non-subjective quantitative analysis, which can be used as diagnostic aids in early detection of dental caries. This study first compares speckle reduction effects on raw OCT image data by implementing spatial-domain and transform-domain speckle filtering. Then region-based contour search and global thresholding techniques examine digital OCT images with possible lesions to identify and highlight the presence of features indicating early stage dental caries. The outputs of these processes, which explore the combination of image restoration and segmentation, can be used to distinguish lesion from normal tissue and determine the characteristics prior to, during, and following treatments. The combination of image processing and analysis techniques in this thesis shows potential of detecting early stage caries lesion successfully.

Covert stroke (CS) comprises lesions in the brain often associated by risk factors such as a diet high in fat, salt, cholesterol and sugar (HFSCS). Developing a rodent model for CS incorporating these characteristics is useful for developing and testing interventions. The purpose of this thesis was to determine if magnetic resonance (MR) can detect brain abnormalities to confirm this model will have the desired anatomical effects. Ex vivo MR showed brain abnormalities for rats with the induced lesions and fed the HFSCS diet. Spectra acquired on the fixed livers had an average percent area under the fat peak relative to the water peak of (20±4)% for HFSCS and (2±2)% for control. In vivo MR images had significant differences between surgeries to induce the lesions (p=0.04). These results show that applying MR identified abnormalities in the rat model and therefore is important in the development of this CS rodent model.

Converting handwritten or machine printed documents into a computer readable format allows more efficient storage and processing. The recognition of machine printed text is very reliable with today's technology, but the recognition of offline handwriting still remains a problem to the research community due to the high variance in handwriting styles. Modern mobile devices are capable of performing complex tasks such as scanning invoices, reading traffic signs, and online handwriting recognition, but there are only a few applications that treat offline handwriting. This thesis investigates the segmentation of handwritten documents into text lines and words, how the legibility of handwriting can be increased by beautification, as well as implementing it for modern mobile devices. Text line and word segmentation are crucial steps towards implementing a complete handwriting recognition system. The results of this thesis show that text line and word segmentation along with handwriting beautification can be implemented successfully for modern mobile devices and a survey concluding that the writing on processed documents is more legible than their unprocessed counterparts. An application for the operating system iOS is developed for demonstration.