Dissertations / Theses on the topic 'Pore segmentation'
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Ding, Nan. "3D Modeling of the Lamina Cribrosa in OCT Data." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS148.
Full textThe lamina cribrosa (LC) is a 3D collagenous mesh in theoptic nerve head that plays a crucial role in themechanisms and diagnosis of glaucoma, the second leading cause of blindness in the world. The LC is composed of so-called “pores”, namely axonal paths within the collagenous mesh, through which the axons pass to reach the brain. In vivo 3D observation of the LC pores is now possible thanks to advances in Optical Coherence Tomography (OCT) technology. In this study, we aim to automatically perform the 3D reconstruction of pore paths from OCT volumes, in order to study the remodeling of the lamina cribrosa during glaucoma and better understand this disease.The limited axial resolution of conventional OCT as well as the low signal to noise ratio (SNR) poses challenges for the robust characterization of axonal paths with enough reliability, knowing that it is difficult even for experts to identify the pores in a single en-face image. To this end, our first contribution introduces an innovative method to register and fuse 2 orthogonal 3D OCT volumes in order to enhance the pores. This is, to our knowledge, the first time that orthogonal OCT volumes are jointly exploited to achieve better image quality. Experimental results demonstrate that our algorithm is robust and leads to accurate alignment.Our second contribution presents a context-aware attention U-Net method, a deep learning approach using partial points annotation for the accurate pore segmentation in every 2D en-face image. This work is also, to the best of our knowledge, the first attempt to look into the LC pore reconstruction problem using deep learning methods. Through a comparative analysis with other state-of-the-art methods, we demonstrate the superior performance of the proposed approach.Our robust and accurate pore registration and segmentation methods provide a solid foundation for 3D reconstruction of axonal pathways, our third contribution. We propose a pore tracking method based on a locally applied parametric active contour algorithm. Our model integrates the characteristics of low intensity and regularity of pores. Combined with the 2D segmentation maps, it enables us to reconstruct the axonal paths in 3D plane by plane. These results pave the way for the calculation of biomarkers characterizing the LC and facilitate medical interpretation
Wagh, Ameya Yatindra. "A Deep 3D Object Pose Estimation Framework for Robots with RGB-D Sensors." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1287.
Full textSeguin, Guillaume. "Analyse des personnes dans les films stéréoscopiques." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEE021/document.
Full textPeople are at the center of many computer vision tasks, such as surveillance systems or self-driving cars. They are also at the center of most visual contents, potentially providing very large datasets for training models and algorithms. While stereoscopic data has been studied for long, it is only recently that feature-length stereoscopic ("3D") movies became widely available. In this thesis, we study how we can exploit the additional information provided by 3D movies for person analysis. We first explore how to extract a notion of depth from stereo movies in the form of disparity maps. We then evaluate how person detection and human pose estimation methods perform on such data. Leveraging the relative ease of the person detection task in 3D movies, we develop a method to automatically harvest examples of persons in 3D movies and train a person detector for standard color movies. We then focus on the task of segmenting multiple people in videos. We first propose a method to segment multiple people in 3D videos by combining cues derived from pose estimates with ones derived from disparity maps. We formulate the segmentation problem as a multi-label Conditional Random Field problem, and our method integrates an occlusion model to produce a layered, multi-instance segmentation. After showing the effectiveness of this approach as well as its limitations, we propose a second model which only relies on tracks of person detections and not on pose estimates. We formulate our problem as a convex optimization one, with the minimization of a quadratic cost under linear equality or inequality constraints. These constraints weakly encode the localization information provided by person detections. This method does not explicitly require pose estimates or disparity maps but can integrate these additional cues. Our method can also be used for segmenting instances of other object classes from videos. We evaluate all these aspects and demonstrate the superior performance of this new method
Madadi, Meysam. "Human segmentation, pose estimation and applications." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/457900.
Full textAutomatic analyzing humans in photographs or videos has great potential applications in computer vision containing medical diagnosis, sports, entertainment, movie editing and surveillance, just to name a few. Body, face and hand are the most studied components of humans. Body has many variabilities in shape and clothing along with high degrees of freedom in pose. Face has many muscles causing many visible deformity, beside variable shape and hair style. Hand is a small object, moving fast and has high degrees of freedom. Adding human characteristics to all aforementioned variabilities makes human analysis quite a challenging task. In this thesis, we developed human segmentation in different modalities. In a first scenario, we segmented human body and hand in depth images using example-based shape warping. We developed a shape descriptor based on shape context and class probabilities of shape regions to extract nearest neighbors. We then considered rigid affine alignment vs. non-rigid iterative shape warping. In a second scenario, we segmented face in RGB images using convolutional neural networks (CNN). We modeled conditional random field with recurrent neural networks. In our model pair-wise kernels are not fixed and learned during training. We trained the network end-to-end using adversarial networks which improved hair segmentation by a high margin. We also worked on 3D hand pose estimation in depth images. In a generative approach, we fitted a finger model separately for each finger based on our example-based rigid hand segmentation. We minimized an energy function based on overlapping area, depth discrepancy and finger collisions. We also applied linear models in joint trajectory space to refine occluded joints based on visible joints error and invisible joints trajectory smoothness. In a CNN-based approach, we developed a tree-structure network to train specific features for each finger and fused them for global pose consistency. We also formulated physical and appearance constraints as loss functions. Finally, we developed a number of applications consisting of human soft biometrics measurement and garment retexturing. We also generated some datasets in this thesis consisting of human segmentation, synthetic hand pose, garment retexturing and Italian gestures.
Chen, Daniel Chien Yu. "Image segmentation and pose estimation of humans in video." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/66230/1/Daniel_Chen_Thesis.pdf.
Full textSandhu, Romeil Singh. "Statistical methods for 2D image segmentation and 3D pose estimation." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37245.
Full textDELERUE, JEAN FRANCOIS. "Segmentation 3d, application a l'extraction de reseaux de pores et a la caracterisation hydrodynamique des sols." Paris 11, 2001. http://www.theses.fr/2001PA112141.
Full textHewa, Thondilege Akila Sachinthani Pemasiri. "Multimodal Image Correspondence." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235433/1/Akila%2BHewa%2BThondilege%2BThesis%281%29.pdf.
Full textCalzavara, Ivan. "Human pose augmentation for facilitating Violence Detection in videos: a combination of the deep learning methods DensePose and VioNetHuman pose augmentation for facilitating Violence Detection in videos: a combination of the deep learning methods DensePose and VioNet." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-40842.
Full textKarabagli, Bilal. "Vérification automatique des montages d'usinage par vision : application à la sécurisation de l'usinage." Phd thesis, Université Toulouse le Mirail - Toulouse II, 2013. http://tel.archives-ouvertes.fr/tel-01018079.
Full textGoudie, Duncan. "Discriminative hand-object pose estimation from depth images using convolutional neural networks." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/discriminative-handobject-pose-estimation-from-depth-images-using-convolutional-neural-networks(f677870a-779f-460a-948d-10fc045e094c).html.
Full textLee, Jehoon. "Statistical and geometric methods for visual tracking with occlusion handling and target reacquisition." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43582.
Full textKong, Longbo. "Accurate Joint Detection from Depth Videos towards Pose Analysis." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157524/.
Full textAziz, Kheir Eddine. "Suivi multi-caméras de personnes dans un environnement contraint." Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM4093.
Full textBlanc, Beyne Thibault. "Estimation de posture 3D à partir de données imprécises et incomplètes : application à l'analyse d'activité d'opérateurs humains dans un centre de tri." Thesis, Toulouse, INPT, 2020. http://www.theses.fr/2020INPT0106.
Full textIn a context of study of stress and ergonomics at work for the prevention of musculoskeletal disorders, the company Ebhys wants to develop a tool for analyzing the activity of human operators in a waste sorting center, by measuring ergonomic indicators. To cope with the uncontrolled environment of the sorting center, these indicators are measured from depth images. An ergonomic study allows us to define the indicators to be measured. These indicators are zones of movement of the operator’s hands and zones of angulations of certain joints of the upper body. They are therefore indicators that can be obtained from an analysis of the operator’s 3D pose. The software for calculating the indicators will thus be composed of three steps : a first part segments the operator from the rest of the scene to ease the 3D pose estimation, a second part estimates the operator’s 3D pose, and the third part uses the operator’s 3D pose to compute the ergonomic indicators. First of all, we propose an algorithm that extracts the operator from the rest of the depth image. To do this, we use a first automatic segmentation based on static background removal and selection of a moving element given its position and size. This first segmentation allows us to train a neural network that improves the results. This neural network is trained using the segmentations obtained from the first automatic segmentation, from which the best quality samples are automatically selected during training. Next, we build a neural network model to estimate the operator’s 3D pose. We propose a study that allows us to find a light and optimal model for 3D pose estimation on synthetic depth images, which we generate numerically. However, if this network gives outstanding performances on synthetic depth images, it is not directly applicable to real depth images that we acquired in an industrial context. To overcome this issue, we finally build a module that allows us to transform the synthetic depth images into more realistic depth images. This image-to-image translation model modifies the style of the depth image without changing its content, keeping the 3D pose of the operator from the synthetic source image unchanged on the translated realistic depth frames. These more realistic depth images are then used to re-train the 3D pose estimation neural network, to finally obtain a convincing 3D pose estimation on the depth images acquired in real conditions, to compute de ergonomic indicators
Cabras, Paolo. "3D Pose estimation of continuously deformable instruments in robotic endoscopic surgery." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD007/document.
Full textKnowing the 3D position of robotized instruments can be useful in surgical context for e.g. their automatic control or gesture guidance. We propose two methods to infer the 3D pose of a single bending section instrument equipped with colored markers using only the images provided by the monocular camera embedded in the endoscope. A graph-based method is used to segment the markers. Their corners are extracted by detecting color transitions along Bézier curves fitted on edge points. These features are used to estimate the 3D pose of the instrument using an adaptive model that takes into account the mechanical plays of the system. Since this method can be affected by model uncertainties, the image-to-3d function can be learned according to a training set. We opted for two techniques that have been improved : Radial Basis Function Network with Gaussian kernel and Locally Weighted Projection. The proposed methods are validated on a robotic experimental cell and in in-vivo sequences
Usher, Kane. "Visual homing for a car-like vehicle." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16309/1/Kane_Usher_Thesis.pdf.
Full textUsher, Kane. "Visual homing for a car-like vehicle." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16309/.
Full textSimó, Serra Edgar. "Understanding human-centric images : from geometry to fashion." Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/327030.
Full textSiempre ha sido una meta fundamental de la visión por computador la comprensión de los seres humanos. Los primeros trabajos se fijaron en objetivos sencillos tales como la detección en imágenes de la posición de los individuos. A medida que la investigación progresó se emprendieron tareas mucho más complejas. Por ejemplo, a partir de la detección de los humanos se pasó a la estimación en dos y tres dimensiones de su postura por lo que la tarea consistía en identificar la localización en la imagen o el espacio de las diferentes partes del cuerpo, por ejemplo cabeza, torso, rodillas, brazos, etc...También los atributos humanos se convirtieron en una gran fuente de interés ya que permiten el reconocimiento de los individuos y de sus propiedades como el género o la edad. Más tarde, la atención se centró en el reconocimiento de la acción realizada. Todos estos trabajos reposan en las investigaciones previas sobre la estimación de las posturas y la clasificación de los atributos. En la actualidad, se llevan a cabo investigaciones de un nivel aún superior sobre cuestiones tales como la predicción de las motivaciones del comportamiento humano o la identificación del tallaje de un individuo a partir de una fotografía. En esta tesis desarrollamos una jerarquía de herramientas que cubre toda esta gama de problemas, desde descriptores de rasgos de bajo nivel a modelos probabilísticos de campos condicionales de alto nivel reconocedores de la moda, todos ellos con el objetivo de mejorar la comprensión de los humanos a partir de imágenes RGB monoculares. Para construir estos modelos de alto nivel es decisivo disponer de una batería de datos robustos y fiables de nivel bajo y medio. En este sentido, proponemos dos descriptores novedosos de bajo nivel: uno se basa en la teoría de la difusión de calor en las imágenes y otro utiliza una red neural convolucional para aprender representaciones discriminativas de trozos de imagen. También introducimos diferentes modelos de bajo nivel generativos para representar la postura humana: en particular presentamos un modelo discreto basado en un gráfico acíclico dirigido y un modelo continuo que consiste en agrupaciones de posturas en una variedad de Riemann. Como señales de nivel medio proponemos dos algoritmos estimadores de la postura humana: uno que estima la postura en tres dimensiones a partir de una estimación imprecisa en el plano de la imagen y otro que estima simultáneamente la postura en dos y tres dimensiones. Finalmente construimos modelos de alto nivel a partir de señales de nivel bajo y medio para la comprensión de la persona a partir de imágenes. En concreto, nos centramos en dos diferentes tareas en el ámbito de la moda: la segmentación semántica del vestido y la predicción del buen ajuste de la prenda a partir de imágenes con meta-datos con la finalidad de aconsejar al usuario sobre moda. En resumen, para extraer conocimiento a partir de imágenes con presencia de seres humanos es preciso construir modelos de alto nivel que integren señales de nivel medio y bajo. En general, el punto crítico para obtener resultados fiables es el empleo y la comprensión de rasgos fuertes. La aportación fundamental de esta tesis es la propuesta de una variedad de algoritmos de nivel bajo, medio y alto para el tratamiento de imágenes centradas en seres humanos que pueden integrarse en modelos de alto nivel, para mejor comprensión de los seres humanos a partir de fotografías, así como abordar problemas planteados por el buen ajuste de las prendas.
Ting, Chen-Kang, and 丁介棡. "Market Segmentation and Service Satisfaction of Electronic Commerce for Port of Kaohsiung." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/01424498989465839392.
Full text國立成功大學
交通管理學系
89
This study seeks to examine the market segmentation and service satisfaction of electronic commerce for Port of Kaohsiung. Firstly, the important and satisfaction levels of service attributes and selection criteria of electronic commerce were conducted. Further, five factors were extracted from service attributes based on a factor analysis. These were defined as: harbor service, enquiry service, communication and support service, port information service as well as information service. In addition, five factors of selection criteria included content factor, facility factor, technological support factor, security factor and response factor were founded in this research. Thirdly, a cluster was used to classify those ocean carriers into three groups. These three groups were characterized as: multiple and technological services oriented firms, information and content oriented firms, as well as port information oriented firms. Finally, the results indicated that schedule search and on-line support service were perceived as most important service attributes and needed to improve in the current electronic commerce services. The use of this framework may be a useful approach for port authority to develop their strategies in electronic commerce market.
Kuo, Hung-chin, and 郭宏志. "Feature Detection in Random Scan Data Using Pole Method for Segmentation in Reverse Engineering." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/21722477390367189164.
Full text國立中正大學
機械工程所
95
For manipulating the scanning database, it is usually transform to STL in the reverse engineering. The NURBS surface is still the major way to cope with scanning data in the conventional industry and the results have to be processed further, for instance, fetching the curve artificially to establish NURBS surface. However, it needs more cost and human power and the different experiences on it might cause different results. In this study, we try to establish a preprocess of NURBS surface -- Segmentation to reduce the error of conventional way due to the different judgements. This research mainly provide a pre-process which establish NURBS surface model - a procedures of segmentation. Then, with the region that is cut apart to build NURBS surface directly. The main purpose of this research directly use the method of the computational geometry to establish the relation of points, and then extract the feature points. Then, we compute a minimum spanning tree (MST) for these feature point to become boundary lines. Finally, found out the different region by using the property of B-rep solid model for the segmentation purpose. By this process could reduce human-made error and simplify the conventional steps. It is expected that this process could shorten the processing time of NURBS surface`s establishment. And under the standard processing, it could ensure the quality of product and has a lot of favor to the industry.
Xu, Changhai 1977. "Steps towards the object semantic hierarchy." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-08-3797.
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GARRO, Valeria. "Image localization and parsing using 3D structure." Doctoral thesis, 2013. http://hdl.handle.net/11562/533354.
Full textThe aim of this thesis is the study of two fundamental problems in computer vision: localization from images and semantic image segmentation. The first contribution of this thesis is the development of a complete system that obtains an accurate and fast localization of a hand-held camera device, leveraging not only on a dataset of registered images but also on the three-dimensional information obtained by a Structure from Motion reconstruction. We exploit the 3D structure under two different aspect: first it is directly involved in the camera registration making available robust 2D-3D correspondences instead of 2D-2D pairs of matched features, furthermore we take advantage of the image clustering computed in the Structure from Motion algorithm during the retrieval step of the localization system improving both robustness and efficiency of the aforementioned algorithmic stage. The second part of the thesis consists in an in-depth analysis of one of the main components of the localization system, the camera pose estimation from 2D-3D correspondences. In particular we present a novel formulation of the Perspective-n-Point problem, also known as exterior orientation, in terms of an instance of anisotropic orthogonal Procrustes problem. The last contribution of the thesis is the proposal of a new approach to semantic image segmentation in urban environment that deeply involves the Structure from Motion 3D structure in terms of label transfer from a pre-labeled image to a query image. The query image can be whether an image belonging to the SfM dataset that does not have any semantic information or an external image that has just been localized by the localization system aforementioned. The label assignment problem is modeled as a Markov random field where the nodes are the superpixels of the query image.
Schoeler, Markus. "Visual Perception of Objects and their Parts in Artificial Systems." Doctoral thesis, 2015. http://hdl.handle.net/11858/00-1735-0000-0023-9669-A.
Full textPatrão, Bruno André Santos. "Biblioteca para Desenvolvimento de Aplicações de Realidade Aumentada Baseada em Marcadores Binários." Master's thesis, 2011. http://hdl.handle.net/10316/99649.
Full textO presente trabalho explora a área da Realidade Aumentada e foca-se na integração de objectos virtuais em ambientes reais em tempo real. O objectivo geral é desenvolver uma biblioteca compreensiva e de fácil utilização para integrar no OpenAR e possibilitar a criação de aplicações de Realidade Aumentada. O elemento chave para a interacção com esta biblioteca são os marcadores simples com um código binário criados para fornecer informação sobre o mundo real em tempo real. São descritos e caracterizados os processos que servem de base para o desenvolvimento deste trabalho, nomeadamente a binarização de imagem, a detecção de marcadores, a extracção e aplicação da pose a objectos virtuais. Os resultados deste trabalho comprovam a importância e utilidade de implementar um sistema de Realidade Aumentada desta natureza em diferentes áreas, tais como a interacção homem-máquina, o entretenimento, a educação, a medicina/psicologia e a indústria. Como trabalho futuro, são propostas melhorias ao nível da visualização e realismo dos ambientes virtuais.
The present work explores the area of Augmented Reality and focus on the integration of virtual objects within real ambient in real time. The main objective is to develop a comprehensive library of easy utilization in order to integrate on OpenAR and improve the possibility of create Augmented Reality applications. The key element for the interaction with that library is simple markers with a binary code created to deliver information about the real world in real time. The processes that compose the basis to the development of this work are here exposed and characterized, namely the image binarization, the markers detection, the extraction and application of pose to virtual objects. This work results prove the importance and utility of an Augmented Reality system implantation of this nature in different areas, such as human-computer interaction, entertainment, education, medicine/psychology and industry. As a proposition for future work, improvements in the visualization and realism of virtual ambient are also presented.
Félix, Inês Dinis. "Deep Learning for Markerless Surgical Navigation in Orthopedics." Master's thesis, 2020. http://hdl.handle.net/10316/92155.
Full textA Artroplastia Total do Joelho (ATJ) é um procedimento cirúrgico realizado em pacientes que sofrem de artrite do joelho. O posicionamento correcto dos implantes está fortemente relacionado com múltiplas variáveis cirúrgicas que têm um impacto tremendo no sucesso da cirurgia. Foram investigados e desenvolvidos sistemas de navegação baseados em computador, com o objetivo de auxiliar o cirurgião a controlar, com precisão, essas variáveis cirúrgicas. Esta tese centra-se na navegação em ATJ e aborda dois problemas que são apontados por muitos como fundamentais para a sua adoção consensual.O primeiro problema é que as tecnologias existentes são muito dispendiosas e requerem incisões ósseas adicionais para a fixação de marcadores, geralmente muito volumosos, interferindo com o típico fluxo cirúrgico. Este trabalho apresenta um sistema de navegação sem marcadores que apoia o cirurgião na execução precisa do procedimento de ATJ. O sistema proposto utiliza uma câmara RGB-D móvel para substituir os sistemas de navegação ópticos existentes, eliminando a necessidade de marcadores. A metodologia apresentada combina uma abordagem eficaz baseada em Deep Learning para segmentar com precisão a superfície óssea com um algoritmo robusto baseado na geometria para registar os ossos com modelos pré-operatórios. O desempenho favorável da nossa metodologia é alcançado através (1) do uso de uma estratégia semi-supervisionada para gerar dados de treino a partir de dados reais de cirurgia ATJ, (2) utilizando técnicas eficazes de aumento de dados para melhorar a capacidade de generalização, e (3) utilizando dados de profundidade adequados. A utilidade deste método completo de registo sem marcadores, que generaliza para diferentes dados intra-operatórios, é evidente e os resultados experimentais mostram um desempenho promissor para ATJ baseada em vídeo.O segundo problema está relacionado com a falta de precisão na localização de pontos de referência no joelho durante a navegação, o que pode levar a erros significativos no posicionamento dos implantes. Esta tese apresenta um método de prova de conceito que utiliza Deep Learning para a detecção automática dos pontos de referência apenas a partir de imagens. O objetivo é fornecer sugestões em tempo real para auxiliar o cirurgião nesta tarefa, o que pode ser útil na tomada de decisões e na redução da variabilidade. A validação experimental num ponto de referência mostra que o método atinge resultados fiáveis, podendo ser feita a sua aplicação aos restantes pontos de referência.
Total Knee Arthroplasty (TKA) is a surgical procedure performed in patients suffering from knee arthritis. The correct positioning of the implants is strongly related to multiple surgical variables that have a tremendous impact on the success of the surgery. Computer-based navigation systems have been investigated and developed in order to assist the surgeon in accurately controlling those surgical variables. This thesis focuses in navigation for TKA and addresses two problems that are pointed by many as fundamental for its broader acceptance. The first problem is that existing technologies are very costly, require additional bone incisions for fixing markers to be tracked, and these markers are usually bulky, interfering with the standard surgical flow. This work presents a markerless navigation system that supports the surgeon in accurately performing the TKA procedure. The proposed system uses a mobile RGB-D camera for replacing the existing optical tracking systems and does not require markers to be tracked. We combine an effective deep learning-based approach for accurately segmenting the bone surface with a robust geometry-based algorithm for registering the bones with pre-operative models. The favorable performance of our pipeline is achieved by (1) employing a semi-supervised labeling approach for generating training data from real TKA surgery data, (2) using effective data augmentation techniques for improving the generalization capability, and (3) using appropriate depth data. The construction of this complete markerless registration prototype that generalizes for unseen intra-operative data is non-obvious, and relevant insights and future research directions can be derived. The experimental results show encouraging performance for video-based TKA. The second problem is related to the lack of accuracy in localizing landmarks during image-free navigation, that can lead to significant errors in implant positioning. This thesis presents a proof-of-concept method that uses deep learning for automatic detection of landmarks from only visual input. The aim is to provide real time suggestions to assist the surgeon in this task, which can be useful in decision making and to reduce variability. Experimental validation with one landmark shows that the method achieves reliable results, and extension to the remaining landmarks can be extrapolated.
Kundu, Jogendra Nath. "Self-Supervised Domain Adaptation Frameworks for Computer Vision Tasks." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5782.
Full textSilva, Tomé Pereira da. "Desenvolvimento de plataforma móvel para futebol robótico." Master's thesis, 2010. http://hdl.handle.net/1822/65406.
Full textA robótica de hoje em dia tem inúmeras aplicações práticas, desde a ajuda prestada ao Homem, até situações em que a precisão e a repetibilidade a torna num grande instrumento de trabalho em diversificadas áreas. Em certos casos, em que o meio ambiente que engloba o agente não é totalmente controlado, este tem que se adaptar ao meio envolvente para finalização da sua determinada tarefa. Esta última situação é a mais complexa, mas é também a situação em que se insere o principal objectivo desta dissertação - a construção de um robô autónomo capaz de jogar futebol. O trabalho apresentado, engloba tanto a concepção como a construção de um protótipo de um robô futebolista, com software capaz de controlar o robô autonomamente, assim como software de apoio às competições. Na construção do robô é analisada desde a estrutura, forma, disposição dos componentes e materiais usados; o software é desenvolvido desde a raiz numa nova estrutura organizada; por fim, mas igualmente importante, é implementado software para a comunicação com hardware, para comunicação em rede, processamento de imagem entre outros módulos necessários ao bom funcionamento do robô. No final, são apresentados alguns aspectos críticos de aperfeiçoamento de todo este trabalho, assim como soluções futuras para os problemas encontrados.
Nowadays, robotics has numerous practical applications, from help to humans, to situations where accuracy and repeatability becomes a great tool to work in several different areas. In some cases, when the agent works on uncontrolled environments, he has to adapt itself completely to that environment or to its particular task. This becomes more complex, but it is also the main goal of this thesis - the construction of an autonomous robot, able to play football coping with the RoboCup rules. This thesis work here presented encloses the robot football player prototype design, with software that can autonomously control the robot, and software to support the competition in which it operates. The robot is analyzed regarding design, structure, shape and components arrangement, as well as the materials used. Software was developed in a new organizational structure from scratch and is also explained on this thesis. Several software modules were created, from the network communication, to hardware control, image processing as well as other modules necessary to manage the real game. In the last chapters, critical aspects are described and discussed, as well as future solutions to problems encountered during this whole process.