abstract: Image segmentation is of great importance and value in many applications. In computer vision, image segmentation is the tool and process of locating objects and boundaries within images. The segmentation result may provide more meaningful image data. Generally, there are two fundamental image segmentation algorithms: discontinuity and similarity. The idea behind discontinuity is locating the abrupt changes in intensity of images, as are often seen in edges or boundaries. Similarity subdivides an image into regions that fit the pre-defined criteria. The algorithm utilized in this thesis is the second category. This study addresses the problem of particle image segmentation by measuring the similarity between a sampled region and an adjacent region, based on Bhattacharyya distance and an image feature extraction technique that uses distribution of local binary patterns and pattern contrasts. A boundary smoothing process is developed to improve the accuracy of the segmentation. The novel particle image segmentation algorithm is tested using four different cases of particle image velocimetry (PIV) images. The obtained experimental results of segmentations provide partitioning of the objects within 10 percent error rate. Ground-truth segmentation data, which are manually segmented image from each case, are used to calculate the error rate of the segmentations. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

In recent years, statistical analyses, algorithms, and modeling of big data have been constrained due to computational complexity. Further, the added complexity of relationships among response and explanatory variables, such as higher-order interaction effects, make identifying predictors using standard statistical techniques difficult. These difficulties are only exacerbated in the case of small sample sizes in some studies. Recent analyses have targeted the identification of interaction effects in big data, but the development of methods to identify higher-order interaction effects has been limited by computational concerns. One recently studied method is the Feasible Solutions Algorithm (FSA), a fast, flexible method that aims to find a set of statistically optimal models via a stochastic search algorithm. Although FSA has shown promise, its current limits include that the user must choose the number of times to run the algorithm. Here, statistical guidance is provided for this number iterations by deriving a lower bound on the probability of obtaining the statistically optimal model in a number of iterations of FSA. Moreover, logistic regression is severely limited when two predictors can perfectly separate the two outcomes. In the case of small sample sizes, this occurs quite often by chance, especially in the case of a large number of predictors. Bhattacharyya distance is proposed as an alternative method to address this limitation. However, little is known about the theoretical properties or distribution of B-distance. Thus, properties and the distribution of this distance measure are derived here. A hypothesis test and confidence interval are developed and tested on both simulated and real data.

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Made available in DSpace on 2015-03-05T14:01:20Z (GMT). No. of bitstreams: 0 Previous issue date: 13 / Nenhuma / O baixo custo dos sistemas de aquisição de imagens e o aumento no poder computacional das máquinas disponíveis têm causado uma demanda crescente pela análise automatizada de vídeo, em diversas aplicações, como segurança, interfaces homem-computador, análise de desempenho esportivo, etc. O rastreamento de objetos através de câmeras de vídeo é parte desta análise, e tem-se mostrado um problema desafiador na área de visão computacional. Este trabalho apresenta uma nova abordagem para o rastreamento de objetos baseada em fragmentos. Inicialmente, a região selecionada para o rastreamento é dividida em sub-regiões retangulares (fragmentos), e cada fragmento é rastreado independentemente. Além disso, o histórico de movimentação do objeto é utilizado para estimar sua posição no quadro seguinte. O deslocamento global do objeto é então obtido combinando os deslocamentos de cada fragmento e o deslocamento previsto, de modo a priorizar fragmentos com deslocamento coerente. Um esquema de atualização é aplicado no modelo / The low cost of image acquisition systems and increase the computational power of available machines have caused a growing demand for automated video analysis in several applications, such as surveillance, human-computer interfaces, analysis of sports performance, etc. Object tracking through the video sequence is part of this analysis, and it has been a challenging problem in the computer vision area. This work presents a new approach for object tracking based on fragments. Initially, the region selected for tracking is divided into rectangular subregions (patches, or fragments), and each patch is tracked independently. Moreover, the motion history of the object is used to estimate its position in the subsequent frames. The overall displacement of the object is then obtained combining the displacements of each patch and the predicted displacement vector in order to priorize fragments presenting consistent displacement. An update scheme is also applied to the model, to deal with illumination and appearance c