Propagação de pontos caracteristicos e suas incertezas utilizando a transformada unscented
Leyza Elmeri Baldo Dorini
DISSERTAÇÃO
Português
(Broch.)
T/UNICAMP D734p
[Propagating feature points and its uncertainty using the unscented transform]
Campinas, SP : [s.n.], 2006.
58f. : il.
Orientador: Siome Klein Goldenstein
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
Resumo: O correto estabelecimento de correspondências entre imagens tomadas de diferentes pontos de vista é um problema fundamental na área de visão computacional, sendo base para diversas tarefas de alto nível, tais como reconstrução 3D e análise de movimento. A grande maioria dos algoritmos de...
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Resumo: O correto estabelecimento de correspondências entre imagens tomadas de diferentes pontos de vista é um problema fundamental na área de visão computacional, sendo base para diversas tarefas de alto nível, tais como reconstrução 3D e análise de movimento. A grande maioria dos algoritmos de rastreamento de características não possui uma incerteza associada a posição estimada das características sendo rastreadas, informação esta de extrema importância, considerando sua vasta aplicabilidade. Exatamente este o foco principal deste trabalho, onde introduzimos um framework genérico que expande algoritmos de rastreamento de tal forma que eles possam propagar também informações de incerteza. Neste trabalho, por questão de simplicidade, utilizamos o algoritmo de rastreamento de características Kanade-Lucas-Tomasi (KLT) para demonstrar as vantagens do nosso método, denominado Unscented Feature Tracking (UFT). A abordagem consiste na introdução de Variáveis Aleatórias Gaussianas (GRVs) para a representação da localização dos pontos característicos, e utiliza a Transformada Unscented com Escala (SUT) para propagar e combinar GRVs. Mostramos uma aplicação do UFT em um procedimento de bundle adjustment, onde a função custo leva em conta a informação das GRVs, fornecendo melhores estimativas. O método é robusto, considerando que identifica e descarta anomalias, que podem comprometer de maneira expressiva o resultado de tarefas que utilizam as correspondências. Experimentos com seqüências de imagens reais e sintéticas comprovam os benefícios do método proposto
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Abstract:
To determine reliable correspondences between a pair of images is a fundamental problem in the computer vision community. It is the foundation of several high level tasks, such as 3D reconstruction and motion analysis. Although there are many feature tracking algorithms, most of them do... Ver mais Abstract:
To determine reliable correspondences between a pair of images is a fundamental problem in the computer vision community. It is the foundation of several high level tasks, such as 3D reconstruction and motion analysis. Although there are many feature tracking algorithms, most of them do not maintain information about the uncertainty of the feature locations' estimates. This information is very useful, since large errors can disturb the results of the correspondence-based tasks. This is the goal of this work, a new generic framework that augments feature tracking algorithms so that they also propagate uncertainty information. In this work, we use the well-known Kanade-Lucas-Tomasi (KLT) feature tracker to demonstrate the benefits of our method, called Unscented Feature Tracking (UFT). The approach consists on the introduction of Gaussian Random Variables (GRVs) for the representation of the features' locations, and on the use of the Scaled Unscented Transform (SUT) to propagate and combine GRVs. We also describe an improved bundle adjustment procedure as an application, where the cost function takes into account the information of the GRVs, and provides better estimates. Experiments with real and synthetic images confirm that UFT improves the quality of the feature tracking process and is a robust method for detect and reject outliers Ver menos
To determine reliable correspondences between a pair of images is a fundamental problem in the computer vision community. It is the foundation of several high level tasks, such as 3D reconstruction and motion analysis. Although there are many feature tracking algorithms, most of them do... Ver mais Abstract:
To determine reliable correspondences between a pair of images is a fundamental problem in the computer vision community. It is the foundation of several high level tasks, such as 3D reconstruction and motion analysis. Although there are many feature tracking algorithms, most of them do not maintain information about the uncertainty of the feature locations' estimates. This information is very useful, since large errors can disturb the results of the correspondence-based tasks. This is the goal of this work, a new generic framework that augments feature tracking algorithms so that they also propagate uncertainty information. In this work, we use the well-known Kanade-Lucas-Tomasi (KLT) feature tracker to demonstrate the benefits of our method, called Unscented Feature Tracking (UFT). The approach consists on the introduction of Gaussian Random Variables (GRVs) for the representation of the features' locations, and on the use of the Scaled Unscented Transform (SUT) to propagate and combine GRVs. We also describe an improved bundle adjustment procedure as an application, where the cost function takes into account the information of the GRVs, and provides better estimates. Experiments with real and synthetic images confirm that UFT improves the quality of the feature tracking process and is a robust method for detect and reject outliers Ver menos
Propagação de pontos caracteristicos e suas incertezas utilizando a transformada unscented
Leyza Elmeri Baldo Dorini
Propagação de pontos caracteristicos e suas incertezas utilizando a transformada unscented
Leyza Elmeri Baldo Dorini
Exemplares
Nº de exemplares: 2
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