Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Estatística, 2018. / Submitted by Raquel Almeida (raquel.df13@gmail.com) on 2018-05-10T18:06:58Z No. of bitstreams: 1 2017_HelenIndianaraSeabraGomes.pdf: 1572147 bytes, checksum: 556db4464b8287cf2e50a1897e7339fb (MD5) / Approved for entry into archive by Raquel Viana (raquelviana@bce.unb.br) on 2018-05-15T19:55:33Z (GMT) No. of bitstreams: 1 2017_HelenIndianaraSeabraGomes.pdf: 1572147 bytes, checksum: 556db4464b8287cf2e50a1897e7339fb (MD5) / Made available in DSpace on 2018-05-15T19:55:33Z (GMT). No. of bitstreams: 1 2017_HelenIndianaraSeabraGomes.pdf: 1572147 bytes, checksum: 556db4464b8287cf2e50a1897e7339fb (MD5) Previous issue date: 2018-05-15 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). / O presente trabalho apresenta um modelo bidimensional não-compensatório da teoria da resposta ao item para lidar com não-respostas intencionais em testes com itens dicotômicos. Uma dimensão fornece informações sobre o comportamento de omissão, chamado de propensão a responder, enquanto a outra dimensão está relacionada à habilidade do indivíduo. O modelo é ajustado aos dados de um exame do tipo high stake (alto risco) feito por 10.822 estudantes do ensino médio que participaram do programa de avaliação seriada da Universidade de Brasília em 2008. Nesse tipo de exame há grande incidência de não-respostas devido a particular forma de correção, em que uma resposta errada anula uma resposta correta. A estimação dos parâmetros dos itens (dificuldade e discriminação) foi feita via Máxima Verossimilhança Marginal. A proficiência e a propensão do candidato foram estimadas pelo método da Esperança Posteriori. Na análise de ajuste dos dados ao modelo foi utilizada a medida de distância de Bhattacharyya como uma alternativa à medida quiquadrado. Em geral, as frequências observadas de acerto foram inferiores às suas respectivas frequências esperadas. Mesmo assim, 40 itens se mostraram aderentes ao modelo ajustado. Observou-se que os candidatos menos proficientes são menos propensos a responder de forma errônea, pois tendem a deixar a resposta em branco. Isso sugere que a decisão de responder ou não seja mais importante do que a decisão de responder corretamente ou não. Dessa forma, este trabalho mostra que a resposta em branco deve ser tratada como uma informação não ignorável, e que não tem relação apenas com a proficiência do candidato, mas também com as características dos itens e o traço latente propensão a responder. / The present work introduces a two-dimensional non-compensatory model of Item Response Theory to deal with intentional non-responses in tests with dichotomous items. One dimension provides information about the behavior of omission, called the propensity to respond, while the other dimension is related to the ability of the individual. The model is adjusted to the data of an examination of the type high stake made by 10.822 students of high school who participated in the program of Evaluation of the University of Brasília in 2008. In this type of examination there is a large incidence of non-responses by its particular form of correction, in which a wrong answer negates a correct answer.The estimation of the items parameters (difficulty and discrimination) was made via maximum Marginal likelihood. The candidate’s proficiency and propensity were estimated by the expected a posteriori method. In the analysis of the adjustment of the data to the model was used the measure of distance of Bhattacharyya as an alternative to the chi-squared measure. In general, the observed frequencies of the hit were lower than their expected frequencies. Even so, 40 items have shown themselves adhering to the adjusted model. It has been observed that less proficient candidates are less likely to respond erroneously because they tend to leave the answer blank. That suggests that the decision to respond or not to respond is more important than the decision to respond correctly or not. In this way, this work shows that the blank answer should be treated as non-ignorable information, and that it is not only related to the candidate’s proficiency, but also to the characteristics of the items and the latent trace propensity to respond.

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

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

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.

No description available.

Automatic speech recognition (ASR) is a well-researched field of study aimed at augmenting the man-machine interface through interpretation of the spoken word. From in-car voice recognition systems to automated telephone directories, automatic speech recognition technology is becoming increasingly abundant in today’s technological world. Nonetheless, traditional audio-only ASR system performance degrades when employed in noisy environments such as moving vehicles. To improve system performance under these conditions, visual speech information can be incorporated into the ASR system, yielding what is known as audio-video speech recognition (AVASR). A majority of AVASR research focuses on lip parameters extraction within controlled environments, but these scenarios fail to meet the demanding requirements of most real-world applications. Within the visual unconstrained environment, AVASR systems must compete with constantly changing lighting conditions and background clutter as well as subject movement in three dimensions. This work proposes a robust still image lip localization algorithm capable of operating in an unconstrained visual environment, serving as a visual front end to AVASR systems. A novel Bhattacharyya-based face detection algorithm is used to compare candidate regions of interest with a unique illumination-dependent face model probability distribution function approximation. Following face detection, a lip-specific Gabor filter-based feature space is utilized to extract facial features and localize lips within the frame. Results indicate a 75% lip localization overall success rate despite the demands of the visually noisy environment.

A taxonomia vegetal atualmente exige um grande esforço dos botânicos, desde o processo de aquisição do espécime até a morosa comparação com as amostras já catalogadas em um herbário. Nesse contexto, o projeto TreeVis surge como uma ferramenta para a identificação de vegetais por meio da análise de atributos foliares. Este trabalho é uma ramificação do projeto TreeVis e tem o objetivo de identificar vegetais por meio da análise do corte transversal de uma folha ampliado por um microscópio. Para tanto, foram extraídas assinaturas da cutícula, epiderme superior, parênquima paliçádico e parênquima lacunoso. Cada assinatura foi avaliada isoladamente por uma rede neural pelo método leave-one-out para verificar a sua capacidade de discriminar as amostras. Uma vez selecionados os vetores de características mais importantes, os mesmos foram combinados de duas maneiras. A primeira abordagem foi a simples concatenação dos vetores selecionados; a segunda, mais elaborada, reduziu a dimensionalidade (três atributos apenas) de algumas das assinaturas componentes antes de fazer a concatenação. Os vetores finais obtidos pelas duas abordagens foram testados com rede neural via leave-one-out para medir a taxa de acertos alcançada pelo sinergismo das assinaturas das diferentes partes da folha. Os experimentos consitiram na identificação de oito espécies diferentes e na identificação da espécie Gochnatia polymorpha nos ambientes Cerrado e Mata Ciliar, nas estações Chuvosa e Seca, e sob condições de Sol e Sombra / Currently, taxonomy demands a great effort from the botanists, ranging from the process of acquisition of the sample to the comparison with the species already classified in the herbarium. For this reason, the TreeVis is a project created to identify vegetal species using leaf attributes. This work is a part of the TreeVis project and aims at identifying vegetal species by analysing cross-sections of leaves amplified by a microscope. Signatures were extract from cuticle, adaxial epiderm, palisade parenchyma and sponge parenchyma. Each signature was analysed by a neural network with the leave-one-out method to verify its ability to identify species. Once the most important feature vectors were selected, two different approachs were adopted. The first was a simple concatenation of the selected feature vectors. The second, and more elaborated approach, consisted of reducing the dimensionality (three attributes only) of some component signatures before the feature vector concatenation. The final vectors obtained by these two approaches were tested by a neural network with leave-one-out to measure the correctness rate reached by the synergism of the signatures of different leaf regions. The experiments resulted in the identification of eight different species and the identification of the Gochnatia polymorpha species in Cerradão and Gallery Forest environments, Wet and Dry seasons, and under Sun and Shadow constraints

A taxonomia vegetal atualmente exige um grande esforço dos botânicos, desde o processo de aquisição do espécime até a morosa comparação com as amostras já catalogadas em um herbário. Nesse contexto, o projeto TreeVis surge como uma ferramenta para a identificação de vegetais por meio da análise de atributos foliares. Este trabalho é uma ramificação do projeto TreeVis e tem o objetivo de identificar vegetais por meio da análise do corte transversal de uma folha ampliado por um microscópio. Para tanto, foram extraídas assinaturas da cutícula, epiderme superior, parênquima paliçádico e parênquima lacunoso. Cada assinatura foi avaliada isoladamente por uma rede neural pelo método leave-one-out para verificar a sua capacidade de discriminar as amostras. Uma vez selecionados os vetores de características mais importantes, os mesmos foram combinados de duas maneiras. A primeira abordagem foi a simples concatenação dos vetores selecionados; a segunda, mais elaborada, reduziu a dimensionalidade (três atributos apenas) de algumas das assinaturas componentes antes de fazer a concatenação. Os vetores finais obtidos pelas duas abordagens foram testados com rede neural via leave-one-out para medir a taxa de acertos alcançada pelo sinergismo das assinaturas das diferentes partes da folha. Os experimentos consitiram na identificação de oito espécies diferentes e na identificação da espécie Gochnatia polymorpha nos ambientes Cerrado e Mata Ciliar, nas estações Chuvosa e Seca, e sob condições de Sol e Sombra / Currently, taxonomy demands a great effort from the botanists, ranging from the process of acquisition of the sample to the comparison with the species already classified in the herbarium. For this reason, the TreeVis is a project created to identify vegetal species using leaf attributes. This work is a part of the TreeVis project and aims at identifying vegetal species by analysing cross-sections of leaves amplified by a microscope. Signatures were extract from cuticle, adaxial epiderm, palisade parenchyma and sponge parenchyma. Each signature was analysed by a neural network with the leave-one-out method to verify its ability to identify species. Once the most important feature vectors were selected, two different approachs were adopted. The first was a simple concatenation of the selected feature vectors. The second, and more elaborated approach, consisted of reducing the dimensionality (three attributes only) of some component signatures before the feature vector concatenation. The final vectors obtained by these two approaches were tested by a neural network with leave-one-out to measure the correctness rate reached by the synergism of the signatures of different leaf regions. The experiments resulted in the identification of eight different species and the identification of the Gochnatia polymorpha species in Cerradão and Gallery Forest environments, Wet and Dry seasons, and under Sun and Shadow constraints