Made available in DSpace on 2014-06-11T19:22:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-12-09Bitstream added on 2014-06-13T19:27:42Z : No. of bitstreams: 1 martins_efo_me_prud.pdf: 7697728 bytes, checksum: 0a4768f80fcc4184fcaa8a4d6ead444b (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Nesta pesquisa é proposta uma metodologia semi-automática para extração de rodovias a partir de um estéreo par de imagens aéreas de baixa resolução e do respectivo Modelo Digital de Terreno, tendo por base a otimização por programação dinâmica no espaço-objeto. A metodologia consiste em um processo iterativo iniciado com pontos sementes fornecidos pelo operador no espaço-imagem, que após transformação para o espaço-objeto passam por ciclos de otimização via Programação Dinâmica até descreverem o eixo da rodovia. O desempenho da metodologia foi testado por meio de experimentos com dados reais, cujos resultados foram avaliados tanto na forma visual (qualitativamente) como numérica (quantitativamente). Os resultados alcançados nos experimentos demonstraram a robustez da metodologia diante de problemas de caráter geométrico e radiométrico comuns na extração semi-automática de rodovias a partir de imagens aéreas. Problemas de oclusão e baixa resposta radiométrica foram minimizados pelo uso de injunções globais, de natureza geométrica, bem como pela redundância e complementação de dados radiométricos provenientes das imagens que compõem o estéreo par. As linhas de busca multiresolução e os critérios de parada das iterações se mostraram como sendo importantes recursos na tentativa de conciliar extração de qualidade com baixo esforço computacional / This research proposes a semi-automatic methodology for road extraction by using a stereo pair of aerial images with low resolution, as well as Digital Terrain Model and dynamic programming in object-space. The methodology consists of an interactive process that starts with seed points provided by the operator in the space-image, which are later projected onto the space-object. Next, cycles of optimization are accomplished by the dynamic programming algorithm until the axis of the highway is correctly described. The performance of the methodology was tested with experiments by using real data, and results were evaluated both visually and numerically. The results achieved in the experiments have demonstrated the robustness of the methodology in face of geometrical and radiometric problems which are common in road extraction. Occlusions and low radiometric responses were minimized by the use of global geometric constraints, as well as the redundancy and complementation of radiometric data from the images that build the stereo pair. The line of multi-resolution search and stopping criteria of the interactions have showed themselves that they have been an important resort in the attempt to reconcile the extraction of high quality with low consumption of computational resources

Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing information from different types of sensors, the accuracy and robustness of the estimates can be increased. Different types of maps are discussed and compared in the thesis. In particular, road maps make use of the fact that roads are highly structured, which allows relatively simple and powerful models to be employed. It is shown how the information of the lane markings, obtained by a front looking camera, can be fused with inertial measurement of the vehicle motion and radar measurements of vehicles ahead to compute a more accurate and robust road geometry estimate. Further, it is shown how radar measurements of stationary targets can be used to estimate the road edges, modeled as polynomials and tracked as extended targets. Recent advances in the field of multiple target tracking lead to the use of finite set statistics (FISST) in a set theoretic approach, where the targets and the measurements are treated as random finite sets (RFS). The first order moment of a RFS is called probability hypothesis density (PHD), and it is propagated in time with a PHD filter. In this thesis, the PHD filter is applied to radar data for constructing a parsimonious representation of the map of the stationary objects around the vehicle. Two original contributions, which exploit the inherent structure in the map, are proposed. A data clustering algorithm is suggested to structure the description of the prior and considerably improving the update in the PHD filter. Improvements in the merging step further simplify the map representation. When it comes to tracking moving targets, the focus of this thesis is on extended targets, i.e., targets which potentially may give rise to more than one measurement per time step. An implementation of the PHD filter, which was proposed to handle data obtained from extended targets, is presented. An approximation is proposed in order to limit the number of hypotheses. Further, a framework to track the size and shape of a target is introduced. The method is based on measurement generating points on the surface of the target, which are modeled by an RFS. Finally, an efficient and novel Bayesian method is proposed for approximating the tire radii of a vehicle based on particle filters and the marginalization concept. This is done under the assumption that a change in the tire radius is caused by a change in tire pressure, thus obtaining an indirect tire pressure monitoring system. The approaches presented in this thesis have all been evaluated on real data from both freeways and rural roads in Sweden. / SEFS -- IVSS / VR - ETT

Made available in DSpace on 2014-06-11T19:30:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-10-31Bitstream added on 2014-06-13T20:47:04Z : No. of bitstreams: 1 gallis_rba_dr_prud.pdf: 3261376 bytes, checksum: 6967d0b5771ef57a837696cfb04efa2f (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Este trabalho propõe uma nova metodologia para extração de rodovias utilizando imagens aéreas digitais. A inovação baseia-se no algoritmo de Programação dinâmica (PD), que nesta metodologia realiza o processo de otimização no espaço objeto, e não no espaço imagem como as metodologias tradicionais de extração de rodovias por PD. A feição rodovia é extraída no espaço objeto, o qual implica um rigoroso modelo matemático, que é necessário para estabelecer os pontos entre o espaço imagem e objeto. Necessita-se que o operador forneça alguns pontos sementes no espaço imagem para descrever grosseiramente a rodovia, e estes pontos devem ser transformados para o espaço objeto para inicialização do processo de otimização por PD. Esta metodologia pode operar em diferentes modos (modo mono e estéreo), e com diversos tipos de imagens, incluindo imagens multisensores. Este trabalho apresenta detalhes da metodologia mono e estéreo e também os experimentos realizados e os resultados obtidos. / This work proposes a novel road extraction methodology from digital images. The innovation is based on the dynamic programming (DP) algorithm to carry out the optimisation process in the object space, instead of doing it in the image space such as the DP traditional methodologies. Road features are traced in the object space, which implies that a rigorous mathematical model is necessary to be established between image and object space points. It is required that the operator measures a few seed points in the image space to describe sparsely and coarsely the roads, which must be transformed into the object space to make possible the initialisation of the DP optimisation process. Although the methodology can operate in different modes (mono-plotting or stereoplotting), and with several image types, including multisensor images, this work presents details of our single and stereo image methodology, along with the experimental results.

Orientador: Aluir Porfírio Dal Poz / Banca: Edson Aparecido Mitishita / Banca: Maurício Galo / Resumo: Nesta pesquisa é proposta uma metodologia semi-automática para extração de rodovias a partir de um estéreo par de imagens aéreas de baixa resolução e do respectivo Modelo Digital de Terreno, tendo por base a otimização por programação dinâmica no espaço-objeto. A metodologia consiste em um processo iterativo iniciado com pontos sementes fornecidos pelo operador no espaço-imagem, que após transformação para o espaço-objeto passam por ciclos de otimização via Programação Dinâmica até descreverem o eixo da rodovia. O desempenho da metodologia foi testado por meio de experimentos com dados reais, cujos resultados foram avaliados tanto na forma visual (qualitativamente) como numérica (quantitativamente). Os resultados alcançados nos experimentos demonstraram a robustez da metodologia diante de problemas de caráter geométrico e radiométrico comuns na extração semi-automática de rodovias a partir de imagens aéreas. Problemas de oclusão e baixa resposta radiométrica foram minimizados pelo uso de injunções globais, de natureza geométrica, bem como pela redundância e complementação de dados radiométricos provenientes das imagens que compõem o estéreo par. As linhas de busca multiresolução e os critérios de parada das iterações se mostraram como sendo importantes recursos na tentativa de conciliar extração de qualidade com baixo esforço computacional / Abstract: This research proposes a semi-automatic methodology for road extraction by using a stereo pair of aerial images with low resolution, as well as Digital Terrain Model and dynamic programming in object-space. The methodology consists of an interactive process that starts with seed points provided by the operator in the space-image, which are later projected onto the space-object. Next, cycles of optimization are accomplished by the dynamic programming algorithm until the axis of the highway is correctly described. The performance of the methodology was tested with experiments by using real data, and results were evaluated both visually and numerically. The results achieved in the experiments have demonstrated the robustness of the methodology in face of geometrical and radiometric problems which are common in road extraction. Occlusions and low radiometric responses were minimized by the use of global geometric constraints, as well as the redundancy and complementation of radiometric data from the images that build the stereo pair. The line of multi-resolution search and stopping criteria of the interactions have showed themselves that they have been an important resort in the attempt to reconcile the extraction of high quality with low consumption of computational resources / Mestre

Resumo: Este trabalho propõe uma nova metodologia para extração de rodovias utilizando imagens aéreas digitais. A inovação baseia-se no algoritmo de Programação dinâmica (PD), que nesta metodologia realiza o processo de otimização no espaço objeto, e não no espaço imagem como as metodologias tradicionais de extração de rodovias por PD. A feição rodovia é extraída no espaço objeto, o qual implica um rigoroso modelo matemático, que é necessário para estabelecer os pontos entre o espaço imagem e objeto. Necessita-se que o operador forneça alguns pontos sementes no espaço imagem para descrever grosseiramente a rodovia, e estes pontos devem ser transformados para o espaço objeto para inicialização do processo de otimização por PD. Esta metodologia pode operar em diferentes modos (modo mono e estéreo), e com diversos tipos de imagens, incluindo imagens multisensores. Este trabalho apresenta detalhes da metodologia mono e estéreo e também os experimentos realizados e os resultados obtidos. / Abstract: This work proposes a novel road extraction methodology from digital images. The innovation is based on the dynamic programming (DP) algorithm to carry out the optimisation process in the object space, instead of doing it in the image space such as the DP traditional methodologies. Road features are traced in the object space, which implies that a rigorous mathematical model is necessary to be established between image and object space points. It is required that the operator measures a few seed points in the image space to describe sparsely and coarsely the roads, which must be transformed into the object space to make possible the initialisation of the DP optimisation process. Although the methodology can operate in different modes (mono-plotting or stereoplotting), and with several image types, including multisensor images, this work presents details of our single and stereo image methodology, along with the experimental results. / Orientador: João Fernando Custódio da Silva / Coorientador: Aluir Porfírio Dal Poz / Banca: Júlio Kiyoshi Hasegawa / Banca: Messias Meneguette Júnior / Doutor