Vision-based positioning systems are founded primarily on a simple image processing technique of identifying various visually significant key-points in an image and relating them to a known coordinate system in a scene. Fiducial markers are used as a means of providing the scene with a number of specific key-points, or features, such that computer vision algorithms can quickly identify them within a captured image. This thesis proposes a reliable vision-based positioning system which utilizes a unique pseudo-random fiducial marker. The marker itself offers 49 distinct feature points to be used in position estimation. Detection of the designed marker occurs after an integrated process of adaptive thresholding, k-means clustering, color classification, and data verification. The ultimate goal behind such a system would be for indoor localization implementation in low cost autonomous mobile platforms.

Vision-based positioning systems are founded primarily on a simple image processing technique of identifying various visually significant key-points in an image and relating them to a known coordinate system in a scene. Fiducial markers are used as a means of providing the scene with a number of specific key-points, or features, such that computer vision algorithms can quickly identify them within a captured image. This thesis proposes a reliable vision-based positioning system which utilizes a unique pseudo-random fiducial marker. The marker itself offers 49 distinct feature points to be used in position estimation. Detection of the designed marker occurs after an integrated process of adaptive thresholding, k-means clustering, color classification, and data verification. The ultimate goal behind such a system would be for indoor localization implementation in low cost autonomous mobile platforms.

Vision-based positioning systems are founded primarily on a simple image processing technique of identifying various visually significant key-points in an image and relating them to a known coordinate system in a scene. Fiducial markers are used as a means of providing the scene with a number of specific key-points, or features, such that computer vision algorithms can quickly identify them within a captured image. This thesis proposes a reliable vision-based positioning system which utilizes a unique pseudo-random fiducial marker. The marker itself offers 49 distinct feature points to be used in position estimation. Detection of the designed marker occurs after an integrated process of adaptive thresholding, k-means clustering, color classification, and data verification. The ultimate goal behind such a system would be for indoor localization implementation in low cost autonomous mobile platforms.

Submitted by Diego Cesar (rasecg3@gmail.com) on 2017-06-26T19:47:01Z No. of bitstreams: 1 main_compressed.pdf: 16459769 bytes, checksum: b7838aeb4e94120d45daddb2c1b3c80e (MD5) / Approved for entry into archive by Flávia Sousa (flaviabs@ufba.br) on 2017-06-28T14:27:38Z (GMT) No. of bitstreams: 1 main_compressed.pdf: 16459769 bytes, checksum: b7838aeb4e94120d45daddb2c1b3c80e (MD5) / Made available in DSpace on 2017-06-28T14:27:38Z (GMT). No. of bitstreams: 1 main_compressed.pdf: 16459769 bytes, checksum: b7838aeb4e94120d45daddb2c1b3c80e (MD5) / Underwater navigation is affected by the lack of GPS due to the attenuation of the electromagnetic signals. Thereby, underwater robots rely on dead reckoning as their main navigation systems. However, localization via dead-reckoning raises uncertainties over time. Consequently, visual and acoustic sensors have been used to increase accuracy in robotic systems navigation, specially when they move in relation to a target object. This level of precision is required, for instance, for object manipulation, inspection, monitoring and docking. This work aims to develop and assess a hybrid visual controller for an autonomous underwater vehicle (AUV) using artificial fiducial markers as reference. Artificial fiducial markers are planar targets, designed to be easily detected by computer vision systems and provide means to estimate the robot’s pose in respect to the marker. They usually have high detection rate and low false positive rate, which are desirable for visual servoing tasks. On this master thesis was evaluated, from among the most popular and open-source marker systems, one that presents the best performance in underwater environments in terms of detection rate, false positives rate, maximum distance and angle for successful detection. Afterwards, the best marker was used for visual servoing purposes in an underwater robot. The firsts experiments were performed on the Gazebo robot simulation environment and, after that, on a real prototype, the FlatFish. Tests on a saltwater tank were performed in order to assess the controller using static and adaptive gains. Finally, sea trials were performed, using the controller that best behaved on the controlled environment in order to assess its performance on a real environment. The tests have shown that the visual controller was able of station-keeping in front of an artificial fiducial marker. Additionally, it was also seen that the adaptive gain brings improvements, mainly because it smooths the robot’s motion on the beginning of the task. / Navegação submarina é afetada pela falta de GPS, devido à atenuação de ondas eletromagnéticas. Por causa disso, os robôs submarinos baseiam-se em sistemas de navegação via odometria e sensores inerciais. Contudo, a localização via esse tipo de abordagem possui uma incerteza associada que cresce com o passar do tempo. Por isso sensores visuais e acústicos são utilizados para aumentar a precisão da navegação de veículos submarinos. Nesse contexto, a utilização de um controlador visual aumenta a precisão dos sistemas robóticos quando se locomovem em relação a um objeto alvo. Esse tipo de precisão é requerida para manipulação de objetos, inspeção, monitoramento e docagem submarina. Esse trabalho tem como objetivo projetar e avaliar um controlador visual híbrido para um veículo submarino autônomo (AUV) utilizando como referência marcos visuais artificiais. Os marcos artificiais são alvos planares projetados para serem facilmente detectados por sistemas de visão computacional, sendo capazes de fornecer meios para estimação da posição do robô em relação ao marco. As suas características de alta taxa de detecção e baixa taxa de falsos positivo são desejáveis para tarefas de controle servo visual. Este trabalho analisou, portanto, dentre os marcos mais populares e de código aberto, aquele que apresenta o melhor desempenho em ambientes submarinos, em termos de taxa de detecção, número de falsos positivos, máxima distância e ângulo para detecção. Posteriormente, o marco que apresentou melhor performance foi utilizado para aplicação de controle visual em um robô submarino. Os primeiros ensaios foram realizados na plataforma de simulação robótica Gazebo e, posteriormente, em um protótipo de AUV real, o FlatFish. Testes em um tanque de água salgada foram realizados visando avaliar a solução proposta utilizando um ganho estático e um ganho adaptativo para o controlador visual. Finalmente, testes no mar foram realizados utilizando o controlador que apresentou os melhores resultados no ambiente controlado, a fim de verificar seu desempenho em um ambiente real. Os testes mostraram que o controlador visual foi capaz de manter o veículo em frente aos marcos visuais artificiais e que o ganho adaptativo trouxe vantagens, principalmente por suavizar a movimentação do robô no início da missão.

Vision-based positioning systems are founded primarily on a simple image processing technique of identifying various visually significant key-points in an image and relating them to a known coordinate system in a scene. Fiducial markers are used as a means of providing the scene with a number of specific key-points, or features, such that computer vision algorithms can quickly identify them within a captured image. This thesis proposes a reliable vision-based positioning system which utilizes a unique pseudo-random fiducial marker. The marker itself offers 49 distinct feature points to be used in position estimation. Detection of the designed marker occurs after an integrated process of adaptive thresholding, k-means clustering, color classification, and data verification. The ultimate goal behind such a system would be for indoor localization implementation in low cost autonomous mobile platforms.

Image-guided neurosurgery interventions are becoming sur- gical procedure routines. We suggest a novel method for automatic marker localization in X-ray images for Leksell SurgiPlan® which is an image-based neurosergical treat- ment planning software provided by Elekta Instrument AB. We implemented an algorithm for fiducial marker localiza- tion based on feature detection, classification and prior geo- metrical knowledge of the markers. Automatic localization ca help to decrease the human error associated with manual registration of these fiducial markers which is the current applied method for X-ray images in Leksell SurgiPlan®.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18452号 / 医博第3907号 / 新制||医||1004(附属図書館) / 31330 / 京都大学大学院医学研究科医学専攻 / (主査)教授 伊達 洋至, 教授 武田 俊一, 教授 富樫 かおり / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19225号 / 医博第4024号 / 新制||医||1010(附属図書館) / 32224 / 京都大学大学院医学研究科医学専攻 / (主査)教授 戸井 雅和, 教授 富樫 かおり, 教授 一山 智 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DGAM

Cette thèse traite de la reconstruction tridimensionnelle d’une scène rigide à partir d’une collection de photographies numériques, dites vues. Le problème traité est connu sous le nom du "calcul de la structure et du mouvement" (structure-and/from-motion) qui consiste à "expliquer" des trajectoires de points dits d’intérêt au sein de la collection de vues par un certain mouvement de l’appareil (dont sa trajectoire) et des caractéristiques géométriques tridimensionnelles de la scène. Dans ce travail, nous proposons les fondements théoriques pour étendre certaines méthodes de calcul de la structure et du mouvement afin d’intégrer comme données d’entrée, des points d’intérêt réels et des points d’intérêt complexes, et plus précisément des images de points cycliques. Pour tout plan projectif, les points cycliques forment une paire de points complexes conjugués qui, par leur invariance par les similitudes planes, munissent le plan projectif d’une structure euclidienne. Nous introduisons la notion de marqueurs cycliques qui sont des marqueurs plans permettant de calculer sans ambiguïté les images des points cycliques de leur plan de support dans toute vue. Une propriété de ces marqueurs, en plus d’être très "riches" en information euclidienne, est que leurs images peuvent être appariées même si les marqueurs sont disposés arbitrairement sur des plans parallèles, grâce à l’invariance des points cycliques. Nous montrons comment utiliser cette propriété dans le calcul projectif de la structure et du mouvement via une technique matricielle de réduction de rang, dite de factorisation, de la matrice des données correspondant aux images de points réels, complexes et/ou cycliques. Un sous-problème critique abordé dans le calcul de la structure et du mouvement est celui de l’auto-calibrage de l’appareil, problème consistant à transformer un calcul projectif en un calcul euclidien. Nous expliquons comment utiliser l’information euclidienne fournie par les images des points cycliques dans l’algorithme d’auto-calibrage opérant dans l’espace projectif dual et fondé sur des équations linéaires. L’ensemble de ces contributions est finalement utilisé pour une application de suivi automatique de caméra utilisant des marqueurs formés par des couronnes concentriques (appelés CCTags), où il s’agit de calculer le mouvement tridimensionnel de la caméra dans la scène à partir d’une séquence vidéo. Ce type d’application est généralement utilisé dans l’industrie du cinéma ou de la télévision afin de produire des effets spéciaux. Le suivi de caméra proposé dans ce travail a été conçu pour proposer le meilleur compromis possible entre flexibilité d’utilisation et précision des résultats obtenus. / The thesis deals with the problem of 3D reconstruction of a rigid scene from a collection of views acquired by a digital camera. The problem addressed, referred as the Structure-from-Motion (SfM) problem, consists in computing the camera motion (including its trajectory) and the 3D characteristics of the scene based on 2D trajectories of imaged features through the collection. We propose theoretical foundations to extend some SfM paradigms in order to integrate real as well as complex imaged features as input data, and more especially imaged circular points. Circular points of a projective plane consist in a complex conjugate point-pair which is fixed under plane similarity ; thus endowing the plane with an Euclidean structure. We introduce the notion of circular markers which are planar markers that allows to compute, without any ambiguity, imaged circular points of their supporting plane in all views. Aside from providing a very “rich” Euclidean information, such features can be matched even if they are arbitrarily positioned on parallel planes thanks to their invariance under plane similarity ; thus increasing their visibility compared to natural features. We show how to benefit from this geometric property in solving the projective SfM problem via a rank-reduction technique, referred to as projective factorization, of the matrix whose entries are images of real, complex and/or circular features. One of the critical issues in such a SfM paradigm is the self-calibration problem, which consists in updating a projective reconstruction into an euclidean one. We explain how to use the euclidean information provided by imaged circular points in the self-calibration algorithm operating in the dual projective space and relying on linear equations. All these contributions are finally used in an automatic camera tracking application relying on markers made up of concentric circles (called C2Tags). The problem consists in computing the 3D camera motion based on a video sequence. This kind of application is generally used in the cinema or TV industry to create special effects. The camera tracking proposed in this work in designed in order to provide the best compromise between flexibility of use and accuracy.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24884号 / 医博第5018号 / 新制||医||1068(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 鈴木 実, 教授 小濱 和貴, 教授 中島 貴子 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM