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Antonakos, Epameinondas. "Robust statistical deformable models." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/56611.
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During the last few years, we have witnessed tremendous advances in the field of 2D Deformable Models for the problem of landmark localization. These advances, which are mainly reported on the task of face alignment, have created two major and opposing families of methodologies. On the one hand, there are the generative Deformable Models that utilize a Newton-type optimization. This family of techniques has attracted extensive research effort during the last two decades, but has lately been criticized of achieving inaccurate performance. On the other hand, there is the currently predominant family of discriminative Deformable Models that treat the problem of landmark localization as a regression problem. These techniques commonly employ cascaded linear regression and have proved to be very accurate. In this thesis, we argue that even though generative Deformable Models are less accurate than discriminative, they are still very valuable for several tasks. In the first part of the thesis, we propose two novel generative Deformable Models. In the second part of the thesis, we show that the combination of generative and discriminative Deformable Models achieves state-of-the-art results on the tasks of (i) landmark localization and (ii) semi-supervised annotation of large visual data.
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Hauth, Michael. "Visual simulation of deformable models." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=97232125X.
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Chen, Xiao Yu. "Feature matching of deformable models /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?MECH%202008%20CHENX.
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Hills, Mark. "3D deformable models for face tracking." Thesis, University of Nottingham, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442306.
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Ferryman, James Michael. "Visual surveillance using 3D deformable models." Thesis, University of Reading, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270279.
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Cheng, Kun. "Deformable models for adaptive radiotherapy planning." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22893.
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Radiotherapy is the most widely used treatment for cancer, with 4 out of 10 cancer patients receiving radiotherapy as part of their treatment. The delineation of gross tumour volume (GTV) is crucial in the treatment of radiotherapy. An automatic contouring system would be beneficial in radiotherapy planning in order to generate objective, accurate and reproducible GTV contours. Image guided radiotherapy (IGRT) acquires patient images just before treatment delivery to allow any necessary positional correction. Consequently, real-time contouring system provides an opportunity to adopt radiotherapy on the treatment day. In this thesis, freely deformable models (FDM) and shape constrained deformable models (SCDMs) were used to automatically delineate the GTV for brain cancer and prostate cancer. Level set method (LSM) is a typical FDM which was used to contour glioma on brain MRI. A series of low level image segmentation methodologies are cascaded to form a case-wise fully automatic initialisation pipeline for the level set function. Dice similarity coefficients (DSCs) were used to evaluate the contours. Results shown a good agreement between clinical contours and LSM contours, in 93% of cases the DSCs was found to be between 60% and 80%. The second significant contribution is a novel development to the active shape model (ASM), a profile feature was selected from pre-computed texture features by minimising the Mahalanobis distance (MD) to obtain the most distinct feature for each landmark, instead of conventional image intensity. A new group-wise registration scheme was applied to solve the correspondence definition within the training data. This ASM model was used to delineated prostate GTV on CT. DSCs for this case was found between 0.75 and 0.91 with the mean DSC 0.81. The last contribution is a fully automatic active appearance model (AAM) which captures image appearance near the GTV boundary. The image appearance of inner GTV was discarded to spare the potential disruption caused by brachytherapy seeds or gold markers. This model outperforms conventional AAM at the prostate base and apex region by involving surround organs. The overall mean DSC for this case is 0.85.
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Xu, Jiaolong. "Domain adaptation of deformable part-based models." Doctoral thesis, Universitat Autònoma de Barcelona, 2015. http://hdl.handle.net/10803/290266.
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La detecció de vianants és crucial per als sistemes d’assistència a la conducció (ADAS).
Disposar d’un classificador precís és fonamental per a un detector de vianants basat
en visió. Al entrenar un classificador, s’assumeix que les característiques de les dades
d’entrenament segueixen la mateixa distribució de probabilitat que la de les dades de
prova. Tot i això, a la pràctica, aquesta assumpció pot no complir-se per diferents
causes. En aquests casos, en la comunitat de visió per computador és cada cop més
comú utilitzar tècniques que permeten adaptar els classificadors existents del seu
entorn d’entrenament (domini d’origen) al nou entorn de prova (domini de destí). En
aquesta tesi ens centrem en l’adaptació de domini dels detectors de vianants basats
en models deformables basats en parts (DPMs). Com a prova de concepte, utilitzem
dades sintètiques com a domini d’origen (món virtual) i adaptem el detector DPM
entrenat en el món virtual per a funcionar en diferents escenaris reals. Començem
explotant al màxim les capacitats de detecció del DPM entrenant en dades del món
virtual, però, tot i això, al aplicar-lo a diferents conjunts del món real, el detector
encara perd poder de discriminació degut a les diferències entre el món virtual i el
real. És per això, que ens centrem en l’adaptació de domini del DPM.
Per començar, considerem un únic domini d’origen per a adaptar-lo a un únic
domini de destí mitjançant dos mètodes d’aprenentatge per lots, l’A-SSVM i el SASSVM.
Després, l’ampliem a treballar amb múltiples (sub-)dominis mitjançant una
adaptació progressiva, utilitzant una jerarquia adaptativa basada en SSVM (HASSVM)
en el procés d’optimització. Finalment, extenem HA-SSVM per a aconseguir
un detector que s’adapti de forma progressiva i sense intervenció humana al domini de
destí. Cal destacar que cap dels mètodes proposats en aquesta tesi requereix visitar
les dades del domini d’origen. L’evaluació dels resultats, realitzada amb el sistema
d’evaluació de Caltech, mostra que el SA-SSVM millora lleugerament respecte el ASSVM
i millora en 15 punts respecte el detector no adaptat. El model jeràrquic
entrenat mitjançant el HA-SSVM encara millora més els resultats de la adaptació de
domini. Finalment, el mètode sequencial d’adaptació de domini ha demostrat que pot
obtenir resultats comparables a la adaptació per lots, però sense necessitat d’etiquetar
manualment cap exemple del domini de destí. L’adaptació de domini aplicada a la
detecció de vianants és de gran importància i és una àrea que es troba relativament
sense explorar. Desitgem que aquesta tesi pugui assentar les bases del treball futur
d’aquesta àrea.La detección de peatones es crucial para los sistemas de asistencia a la conducción (ADAS). Disponer de un clasificador preciso es fundamental para un detector de peatones basado en visión. Al entrenar un clasificador, se asume que las características de los datos de entrenamiento siguen la misma distribución de probabilidad que las de los datos de prueba. Sin embargo, en la práctica, esta asunción puede no cumplirse debido a diferentes causas. En estos casos, en la comunidad de visión por computador cada vez es más común utilizar técnicas que permiten adaptar los clasificadores existentes de su entorno de entrenamiento (dominio de origen) al nuevo entorno de prueba (dominio de destino). En esta tesis nos centramos en la adaptación de dominio de los detectores de peatones basados en modelos deformables basados en partes (DPMs). Como prueba de concepto, usamos como dominio de origen datos sintéticos (mundo virtual) y adaptamos el detector DPM entrenado en el mundo virtual para funcionar en diferentes escenarios reales. Comenzamos explotando al máximo las capacidades de detección del DPM entrenado en datos del mundo virtual pero, aun así, al aplicarlo a diferentes conjuntos del mundo real, el detector todavía pierde poder de discriminaci ón debido a las diferencias entre el mundo virtual y el real. Es por ello que nos centramos en la adaptación de dominio del DPM. Para comenzar, consideramos un único dominio de origen para adaptarlo a un único dominio de destino mediante dos métodos de aprendizaje por lotes, el A-SSVM y SA-SSVM. Después, lo ampliamos a trabajar con múltiples (sub-)dominios mediante una adaptación progresiva usando una jerarquía adaptativa basada en SSVM (HA-SSVM) en el proceso de optimización. Finalmente, extendimos HA-SSVM para conseguir un detector que se adapte de forma progresiva y sin intervención humana al dominio de destino. Cabe destacar que ninguno de los métodos propuestos en esta tesis requieren visitar los datos del dominio de origen. La evaluación de los resultados, realizadas con el sistema de evaluación de Caltech, muestran que el SA-SSVM mejora ligeramente respecto al A-SSVM y mejora en 15 puntos respecto al detector no adaptado. El modelo jerárquico entrenado mediante el HA-SSVM todavía mejora más los resultados de la adaptación de dominio. Finalmente, el método secuencial de adaptación de domino ha demostrado que puede obtener resultados comparables a la adaptación por lotes pero sin necesidad de etiquetar manualmente ningún ejemplo del dominio de destino. La adaptación de domino aplicada a la detección de peatones es de gran importancia y es un área que se encuentra relativamente sin explorar. Deseamos que esta tesis pueda sentar las bases del trabajo futuro en esta área.
On-board pedestrian detection is crucial for Advanced Driver Assistance Systems (ADAS). An accurate classi cation is fundamental for vision-based pedestrian detection. The underlying assumption for learning classi ers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classi ers. However, in practice, there are di erent reasons that can break this constancy assumption. Accordingly, reusing existing classi ers by adapting them from the previous training environment (source domain) to the new testing one (target domain) is an approach with increasing acceptance in the computer vision community. In this thesis we focus on the domain adaptation of deformable part-based models (DPMs) for pedestrian detection. As a prof of concept, we use a computer graphic based synthetic dataset, i.e. a virtual world, as the source domain, and adapt the virtual-world trained DPM detector to various real-world dataset. We start by exploiting the maximum detection accuracy of the virtual-world trained DPM. Even though, when operating in various real-world datasets, the virtualworld trained detector still su er from accuracy degradation due to the domain gap of virtual and real worlds. We then focus on domain adaptation of DPM. At the rst step, we consider single source and single target domain adaptation and propose two batch learning methods, namely A-SSVM and SA-SSVM. Later, we further consider leveraging multiple target (sub-)domains for progressive domain adaptation and propose a hierarchical adaptive structured SVM (HA-SSVM) for optimization. Finally, we extend HA-SSVM for the challenging online domain adaptation problem, aiming at making the detector to automatically adapt to the target domain online, without any human intervention. All of the proposed methods in this thesis do not require revisiting source domain data. The evaluations are done on the Caltech pedestrian detection benchmark. Results show that SA-SSVM slightly outperforms A-SSVM and avoids accuracy drops as high as 15 points when comparing with a non-adapted detector. The hierarchical model learned by HA-SSVM further boosts the domain adaptation performance. Finally, the online domain adaptation method has demonstrated that it can achieve comparable accuracy to the batch learned models while not requiring manually label target domain examples. Domain adaptation for pedestrian detection is of paramount importance and a relatively unexplored area. We humbly hope the work in this thesis could provide foundations for future work in this area.
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Xiang, Guofu. "Automatic 3D facial modelling with deformable models." Thesis, Robert Gordon University, 2012. http://hdl.handle.net/10059/807.
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Facial modelling and animation has been an active research subject in computer graphics since the 1970s. Due to extremely complex biomechanical structures of human faces and people’s visual familiarity with human faces, modelling and animating realistic human faces is still one of greatest challenges in computer graphics. Since we are so familiar with human faces and very sensitive to unnatural subtle changes in human faces, it usually requires a tremendous amount of artistry and manual work to create a convincing facial model and animation. There is a clear need of developing automatic techniques for facial modelling in order to reduce manual labouring. In order to obtain a realistic facial model of an individual, it is now common to make use of 3D scanners to capture range scans from the individual and then fit a template to the range scans. However, most existing template-fitting methods require manually selected landmarks to warp the template to the range scans. It would be tedious to select landmarks by hand over a large set of range scans. Another way to reduce repeated work is synthesis by reusing existing data. One example is expression cloning, which copies facial expression from one face to another instead of creating them from scratch. This aim of this study is to develop a fully automatic framework for template-based facial modelling, facial expression transferring and facial expression tracking from range scans. In this thesis, the author developed an extension of the iterative closest points (ICP) algorithm, which is able to match a template with range scans in different scales, and a deformable model, which can be used to recover the shapes of range scans and to establish correspondences between facial models. With the registration method and the deformable model, the author proposed a fully automatic approach to reconstructing facial models and textures from range scans without re-quiring any manual interventions. In order to reuse existing data for facial modelling, the author formulated and solved the problem of facial expression transferring in the framework of discrete differential geometry. The author also applied his methods to face tracking for 4D range scans. The results demonstrated the robustness of the registration method and the capabilities of the deformable model. A number of possible directions for future work were pointed out.
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Yeo, Si Yong. "Implicit deformable models for biomedical image segmentation." Thesis, Swansea University, 2011. https://cronfa.swan.ac.uk/Record/cronfa42416.
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In this thesis, new methods for the efficient segmentation of images are presented. The proposed methods are based on the deformable model approach, and can be used efficiently in the segmentation of complex geometries from various imaging modalities. A novel deformable model that is based on a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions is presented. This external force field is based on hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contributes to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and give the deformable model a high invariance in initialization configurations. The voxel interactions across the whole image domain provides a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force held allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, it is shown that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. A comparative study on the segmentation of various geometries with different topologies from both synthetic and real images is provided, and the proposed method is shown to achieve significant improvements against several existing techniques. A robust framework for the segmentation of vascular geometries is described. In particular, the framework consists of image denoising, optimal object edge representation, and segmentation using implicit deformable model. The image denoising is based on vessel enhancing diffusion which can be used to smooth out image noise and enhance the vessel structures. The image object boundaries are derived using an edge detection technique which can produce object edges of single pixel width. The image edge information is then used to derive the geometric interaction field for optimal object edge representation. The vascular geometries are segmented using an implict deformable model. A region constraint is added to the deformable model which allows it to easily get around calcified regions and propagate across the vessels to segment the structures efficiently. The presented framework is ai)plied in the accurate segmentation of carotid geometries from medical images. A new segmentation model with statistical shape prior using a variational approach is also presented in this thesis. The proposed model consists of an image attraction force that propagates contours towards image object boundaries, and a global shape force that attracts the model towards similar shapes in the statistical shape distribution. The image attraction force is derived from gradient vector interactions across the whole image domain, which makes the model more robust to image noise, weak edges and initializations. The statistical shape information is incorporated using kernel density estimation, which allows the shape prior model to handle arbitrary shape variations. It is shown that the proposed model with shape prior can be used to segment object shapes from images efficiently.
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Heap, Anthony James. "Learning deformable shape models for object tracking." Thesis, University of Leeds, 1997. http://etheses.whiterose.ac.uk/1275/.
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The use of computer vision to locate or track objects in images has applications in a diversity of domains. It is generally recognised that the analysis of objects of interest is eased significantly by making use of models of objects. In many cases, the strongest visual feature of an object is its shape. Also, many objects of interest are non-rigid, or have a non-rigid appearance with respect to a particular viewpoint. For these reasons, there is much interest in the construction of, and tracking with, deformable shape models. A common approach to building such a model is to apply statistics to a set of real-life training examples of an object in order to learn shape and deformation characteristics. Such methods have proved successful in many specific applications; however, they can experience inadequacies in the general case. For example, objects which exhibit non-linear deformations give rise to models which are not compact and not specific: in the process of capturing the range of valid shapes, invalid shapes also become incorporated into the model. This effect is particularly pronounced when building models from automatically-gathered training data. Also, in tracking, smooth movement and deformation is generally assumed, but is not always the case: the apparent shape of an object can change discontinuously over time due to, for example, rotations in 3D. The work in this thesis addresses the above problems. Two extensions to current statistical methods are described. The first makes use of polar coordinates to improve the modelling of objects which bend or pivot. The second uses a hierarchical approach to model more general complex deformations; non-linearities are broken down into smaller linear pieces in order to improve model specificity. In particular, this greatly improves the modelling of objects from automatically-gathered training data. A new approach to tracking which complements the latter of these models is also described. Learned object shape dynamics are combined with stochastic tracking to produce a system which can track from automatically-generated models, as well as being able to handle discontinuous shape changes. Examples are given of the use of these techniques, predominantly in the domain of hand tracking. In particular, it is shown how it is possible to track 3D objects purely from 2D models of their silhouettes.
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Baumberg, Adam. "Learning deformable models for tracking human motion." Thesis, University of Leeds, 1996. http://etheses.whiterose.ac.uk/1266/.
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The analysis and automatic interpretation of images containing moving non-rigid objects, such as walking people, has been the subject of considerable research in the field of computer vision and pattern recognition. In order to build fast and reliable systems some kind of prior model is generally required. A model enables the system to cope with situations where there is considerable background clutter or where information is missing from the image data. This may be due to imaging errors (e.g. blurring due to motion) or due to part of an object becoming hidden from view. Conventional approaches to the problem of tracking non-rigid objects require complex hand-crafted models which are not easily adapted to different problems. A more recent approach uses training information to build models for image analysis. This thesis extends this approach by building flexible 2D models, automatically, from sequences of training images. Efficient methods are described for using the resulting models for real time contour tracking using optimal linear filtering techniques. The method is further extended by incorporating a feedback scheme to generate a more compact linear model which is shown to be more robust and accurate for tracking. Models of the shape of an object do not utilise the temporal information contained within the training sequences. A novel method is described for automatically learning a spatiotemporal, physically-based model that allows the system to accurately predict the expected change in object shape over time. This approach is shown to increase the reliability of the system, requiring only a modest increase in computational processing. The system can be automatically trained on video sequences to learn constraints on the apparent shape and motion of a particular non-rigid object in a particular environment. Results show the system is capable of tracking several walking pedestrians in real time without the use of expensive dedicated hardware. The output from this system has potential uses in the areas of surveillance, animation and gait analysis.
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McInerney, Timothy John. "Topologically adaptable deformable models for medical image analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0010/NQ28015.pdf.
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Moutsopoulos, Konstantinos. "Physically deformable models for simulation of laparoscopic surgery." Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339157.
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Chalana, Vikram. "Deformable models for segmentation of medical ultrasound images /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/8025.
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Yao, Miaojun. "3D Printable Designs of Rigid and Deformable Models." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1502906675481174.
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Hughes, H. W. "Recognition of three dimensional objects using deformable models." Thesis, University of Edinburgh, 1992. http://hdl.handle.net/1842/19857.
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This thesis considers the problem of efficiently identifying and locating instances of classes of three dimensional objects by matching them with a single generic model that represents that entire class of object. Since a member of an object class will normally differ from the prototype that represents the class, the approach used here is to allow the model to stretch, or deform, to fit the object. The input image data may contain one or more objects of unknown identity and location, each of which is assumed to belong to a class of object for which there is a corresponding model. Both objects and models are represented by three dimensional surface data with the object data pre-segmented into surfaces of uniform curvature. The process of deforming and matching the models to the object data is achieved in two stages. In the first stage combinations of object surfaces are formed and a search made for suitable object to model correspondences. Simple constraints are developed to reduce the search space to an acceptable size. When a correspondence is achieved, an initial estimate of the stretch required in the model is made and the model that contains those surfaces is selected for further matching. Because only those models for which there is evidence in the image are selected for further matching, the search space is further reduced. The second stage of the process involves taking those models selected in the first stage and performing a rigorous geometric search for any remaining model to object correspondences. As part of this process, the locations of the objects in the image are predicted and the deformation parameters refined as new correspondences are found. The location and deformation parameters provide further constraints for the geometric search, reducing the search space still more. Recognition is demonstrated with a variety of objects, both synthetic and real, and the results discussed. The use of deformable models in object recognition was found to be a good means by which to represent and match objects from classes showing three types of deformation - scale, stretch and small variation. The model deformation as formulated enabled the identity of the corresponding objects and their parameters of deformation to be determined with accuracy and efficiency.
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Yang, Baofen. "Geometric deformable models using the level set method." Mémoire, Université de Sherbrooke, 2005. http://savoirs.usherbrooke.ca/handle/11143/4664.
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Geometric deformable models for active contours have brought tremendous impact to classical problems in image processing such as providing ways to devise efficient computational algorithms for automatic segmentation. This is achieved by using the level set method, which allows handling automatic changes in topology while providing a framework for very fast numerical schemes. However, topological flexibility is not desired when an object with known topology is sought. It is natural to capture the target in a way that gives the correct topology. A geometric deformable model with topology preserving is developed that can guarantee the topology will be preserved while all the computational advantages of the level set approach are maintained. A key issue in object detection using the shape of the object’s boundary and surface reconstruction using slice contours is the ability to identify the complete boundaries of the segmented objects in the scene. The segmentation results provided by geometric deformable models are usually dependent on the contour initialization, and in most cases, the results of the segmentation will only provide partial objects boundaries. A new method based on digital topology is proposed to detect the complete boundary information of the segmented objects. By carrying out a topological analysis of the objects, this method can provide the right initialization that can capture all the boundaries of the objects in certain cases.
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Leymarie, Frédéric. "Tracking and describing deformable objects using active contour models." Thesis, McGill University, 1990. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=59540.
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In this thesis we consider a number of issues in developing techniques and algorithms to automate the visual tracking of deformable objects in the plane. We have applied these techniques in cell locomotion and tracking studies. First, we consider the segmentation of a noisy intensity image and the tracking of a nonrigid object. Second, we consider the shape analysis of an amorphous object. In evaluating these problems, we explore a new technique based on an active contour model commonly called a "snake". We present a detailed analysis of the snake model, emphasizing its limitations and shortcomings, and propose various improvements to the original description of the model. Then, we study the two complementary types of shape descriptors: boundary- and region-based. We propose to combine these within the context of the grassfire transform. Two new algorithms are described. First, we present a contour segmentation technique using mathematical morphology on the curvature function. Accurate localization for different scales of curvature features is achieved. Second, the snake model is used to simulate the grassfire transform using the previously extracted contour features.
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Rodrigues, Maria Andreia Formico. "Deformable models for simulating the upper airways during laryngoscopy." Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313157.
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Zhu, Hui, and 朱暉. "Deformable models and their applications in medical image processing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31238075.
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Zhu, Hui. "Deformable models and their applications in medical image processing /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20717970.
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Chang, Xianglong. "Semi-automatic fitting of deformable 3D models to 2D sketches." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/797.
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We present a novel method for building 3D models from a user sketch. Given a 2D sketch as input, the approach aligns and deforms a chosen 3D template model to match the sketch. This is guided by a set of user-specified correspondences and an algorithm that deforms the 3D model to match the sketched profile. Our primary contribution is related to fitting the 3D deformable geometry to the 2D user sketch. We demonstrate our technique on several examples.
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Bowden, Richard. "Learning non-linear models of shape and motion." Thesis, Brunel University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311245.
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Rajamani, Kumar T. "Three dimensional surface extrapolation from sparse data using deformable bone models /." Bern : [s.n.], 2006. http://opac.nebis.ch/cgi-bin/showAbstract.pl?sys=000279098.
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Fong, Philip. "Data-based models for deformable objects : sensing, acquisition, and interactive playback /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
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Bistoquet, Arnaud. "Cardiac motion recovery from magnetic resonance images using incompressible deformable models." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24628.
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Thesis (Ph.D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008.Committee Chair: Skrinjar, Oskar; Committee Member: Oshinski, John; Committee Member: Tannenbaum, Allen; Committee Member: Vela, Patricio; Committee Member: Yezzi, Anthony
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Okada, Toshihiro. "A study of a ventricular motion in cardiac MRI using deformable models." Connect to resource, 2007. http://hdl.handle.net/1811/24619.
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Thesis (Honors)--Ohio State University, 2007.Title from first page of PDF file. Document formatted into pages: contains 37 p.; also includes graphics. Includes bibliographical references (p. 32-33). Available online via Ohio State University's Knowledge Bank.
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Li, Zhiyong. "Simulation of solute and fluid transport in deformable tissues using poroelastic models." Thesis, Queen Mary, University of London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414484.
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Ruckert, Daniel. "Segmentation and tracking in cardiovascular images using geometrically deformable models and templates." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286210.
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Chen, Zhibin. "Segmentation of MRI images using non parametric deformable models integrating fuzzy technique." Reims, 2009. http://theses.univ-reims.fr/exl-doc/GED00001122.pdf.
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L'objectif de la thèse est de développer une méthode automatique pour segmenter les tissus cérébraux (la matière grise, la matière blanche et le liquide céphalo-rachidien) à partir des images IRM, fournissant ainsi des mesures quantitatives et précises du cerveau. Dans cette thèse, nous avons développé trois modèles déformables non-paramétriques en intégrant l'information statistique et l’information floue des images pour segmenter le cerveau en différents types de tissus. Nous présentons d'abord une méthode basée sur l’analyse de l'histogramme. La répartition de l'intensité des images est modélisée par le modèle de mélanges gaussiens (MMG). Les paramètres du MMG sont estimés par l’algorithme «Expectation Maximization». Ensuite, ils sont utilisés pour guider l'évolution des courbes pour atteindre la segmentation des tissus cérébraux. Nous proposons ensuite une amélioration d’un algorithme basé sur les contours actifs orientés région avec la contrainte géométrique. Grâce à la nouvelle expression proposée, il permet de résoudre le problème de stabilité sous-jacente associé à l'algorithme d’origine, et réalise une convergence rapide. Enfin, nous présentons une segmentation de multi-classes en intégrant une segmentation floue dans la méthode level sets. Elle utilise un ensemble d'équations différentielles ordinaires. Chacune d'elles représente une classe à segmenter. Cette approche réduit la complexité de calcul par rapport à l'algorithme multi-phase existant, permettant donc d’accélérer la vitesse de convergence. Toutes les méthodes ont été évaluées avec des images IRM simulées et réelles. Les analyses quantitatives sont données. Les résultats sont très encourageantsThe research goal of this thesis is to develop an automatic segmentation method to segment brain MRI images into different tissues (gray matter, white matter, and cerebrospinal fluid), providing quantitative and precise brain measurements. In this dissertation, we have developed three non-parametric deformable models integrating statistical information and fuzzy information of images to segment the brain into different tissue types from multi types of MRI images. We firstly present a histogram analysis based algorithm, where the intensity distribution of the MRI images is modeled via the mixture Gaussian model (MGM). The parameters of components in MGM are estimated via the Expectation Maximization (EM) algorithm. Then the estimated parameters are used to guide the evolution of the level set curves to achieve the brain tissue segmentation. We then propose an improved algorithm to region-based geometric active contour. Thanks to the new regional term, the new algorithm solves the underlying stability problem associated with the original algorithm, and achieves convergence with less iteration number compared with the original algorithm. Finally, we present a multiclass algorithm by integrating fuzzy segmentation with the level set methods. The algorithm uses a set of ordinary differential equations; each of them represents a class to be segmented. The multiclass algorithm reduces the computational complexity compared with the existing multiphase algorithm, so speeds up the convergence rate. All algorithms are evaluated with simulated and real MRI images, and quantitative analyses are provided. The results are very encouraging
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Pereira, Danillo Roberto 1984. "Fitting 3D deformable biological models to microscope images = Alinhamento de modelos tridimensionais usando imagens de microscopia." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275627.
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Orientador: Jorge StolfiTese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: Nesta tese descrevemos um algoritmo genérico (que denominamos MSFit) capaz de estimar a pose e as deformações de modelos 3D de estruturas biológicas (bactérias, células e etc.) em imagens obtidas por meio de microscópios óticos ou de varredura eletrônica. O algoritmo usa comparação multi-escala de imagens utilizando uma métrica sensível ao contorno; e um método original de otimização não-linear. Nos nossos testes com modelos de complexidade moderada (até 12 parâmetros) o algoritmo identifica corretamente os parâmetros do modelo em 60-70% dos casos com imagens reais e entre 80-90% dos casos com imagens sintéticas
Abstract: In this thesis we describe a generic algorithm (which we call MSFit) able to estimate the pose and deformations of 3D models of biological structures (bacteria, cells, etc.) with images obtained by optical and scanning electron microscopes. The algorithm uses an image comparison metric multi-scale, that is outline-sensitive, and a novel nonlinear optimization method. In our tests with models of moderate complexity (up to 12 parameters) the algorithm correctly identifies the model parameters in 60-70 % of the cases with real images and 80-90 % of the cases with synthetic images
Doutorado
Ciência da Computação
Doutor em Ciência da Computação
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Santana, Anderson Marques de. "Contornos deformáveis paramétricos adaptativos." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-10082010-134550/.
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Segundo a definição original de MCINERNEY & TERZOPOULOS (1995), modelos deformáveis são curvas ou superfícies formadas por pontos conectados que simulam corpos elásticos. Por superarem muitas limitações associadas ao procedimento manual e às técnicas tradicionais de processamento, os contornos deformáveis têm se popularizado. Ainda que o uso dos contornos deformáveis seja vasto e crescente, aspectos relevantes da teoria ainda têm demandado atenção. Muitas referências têm sido feitas às limitações da técnica impostas sobretudo pelo seu processo evolutivo. A convergência a mínimos locais e o agrupamento indesejado de pontos, por exemplo, limitam o emprego da técnica em cenários ruidosos e complexos como os encontrados em reservatórios de petróleo. Esse trabalho apresenta uma abordagem inédita às limitações dos contornos deformáveis. Pela definição de um segundo problema de minimização são definidas distâncias ótimas dos pontos do contorno deformável segundo critério de optimalidade que contempla as particularidades do contorno buscado. Os resultados demonstram que a técnica proposta é provedora de maior enquadramento entre o contorno buscado e o identificado, define solução definitiva aos problemas do agrupamento e espalhamento indesejados de pontos, aumenta a efetividade dos contornos deformáveis em regiões côncavas e, em acréscimo, define metodologia unicamente capaz de dotar os contornos deformáveis de sensibilidade quanto às particularidades de contorno.According to the original definition of Terzopoulos, deformable models are curves or surfaces formed by connected points that simulate elastic bodies. By overcoming many limitations associated with the manual procedure and the traditional techniques of processing, deformable contours have become popular. Although the use of deformable contours is vast and growing aspects of the theory still demand attention. Many references have been made to the limitations of the technique imposed by the process evolution process. The convergence to minimum and unwanted bundling points, for example, limit the use of the technic on noisy and complex scenarios as those found in oil reservoirs. This work presents a novel approach to the limitations of deformable contours. By the definition of a second problem of minimization are defined optimal distances of the points of deformable contour according to a optimality criterion that incorporates features of the contour sought. The results show that the proposed technique peovides a larger framework between the contour sought and identified, defines a permanent solution to the problems of grouping and unwanted scattering of points, increases the effectiveness of deformable contours in concave regions and, in addition, defines methodology only able to provide the contours deformable sensitivity about the peculiarities of the contour.
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Shen, Jian Kun. "Registration of images containing rigid structures using deformable models and its medical application." Thesis, University of Central Lancashire, 2005. http://clok.uclan.ac.uk/20149/.
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This thesis presents research and development in registration of images containing rigid structures using deformable models. In the existing methods of deformable image registration, the feature based methods using purely interpolation are often used under the assumption that all parts of the image can be deformed non-linearly. However, it is not applicable for some images, such as medical images which contain some bony structures. Therefore, it is more reasonable in image registration to treat the parts representing rigid structures differently from the remaining parts. A novel method is proposed in this thesis that uses a flexible spring-mass system to provide a good representation of image information. Anatomical structures are represented by spring masses and their interconnecting springs located along the structure boundaries. Hence, the system can treat some pre-selected parts of an image as rigid structures, whilst it is possible to elastically deform the rest of the image. The deformation caused by the displacement of the features is modelled by the spring-mass system via the motion equation. To ensure high quality registration, the image content is introduced as external forces acting on the masses and is iteratively employed into the motion equation to achieve a better and better registration result (It should be noted that the improvement in registration quality is limited by the quality and content of original images). The performance of the proposed method is shown using simulated data, and is validated using real Computed Tomography (CT) images. Objective measures and images that enable subjective evaluation of the new technique are provided. The proposed method is fast, robust and offers high registration accuracy. This is due to the following developments: • Innovatory approach based on the spring-mass system for image registration. • A new spring force formula to support the new method. • External forces based on image content are employed and applied via an iterative approach to ensure the high quality registration. • A fast initialisation method for the spring-mass system generation. • A fast interpolation technique to reduce the computation time in the iterative registration stage.
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Taquee, Farheen. "Deformable prostate registration from MR and TRUS images using surface error driven FEM models." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/38195.
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TransRectal Ultrasound (TRUS) is used for image guidance during prostate biopsy and for treatment planning of brachytherapy due to low cost and accessibility in operating room. However, tumors have better visibility in Magnetic Resonance (MR) images. The fusion of TRUS and MR images of the prostate can aid with the diagnosis and treatment planning for prostate cancer and with post-brachytheraphy quality assurance. We developed a 3D deformable registration method using the segmentations obtained from TRUS and MR images and a biomechanical model that employs stiffness values derived from elastography. The segmented source volume is meshed and a linear finite element model is created for it. This volume is deformed to the target image volume by applying surface forces computed by assuming a negative relative pressure between the non-overlapping and the overlapping regions of the volumes. This pressure drives the model to increase the volume overlap until the surfaces are aligned. We tested our algorithm on prostate surfaces extracted from postoperative MR and TRUS images for 14 patients and pre-operative MR and TRUS images for 4 patients, using a model with elasticity within the range reported in the literature for the prostate. We used three evaluation metrics for validation: the Dice Similarity Coefficient (DSC) (ideally equal to 1.0), the volume change in source surface during registration, and the Target Registration error (TRE) defined as the mean distance between landmarks such as urethrae and calcifications. For post-operative images, we obtained a DSC of 0.96±0.02 and a TRE of 1.5±1.4mm. The change in the volume of the source surface was 1.5±1.4%. For pre-operative images, we obtained the DSC of 0.96±0.01 and a TRE of 1.3±0.8mm. The change in the volume of the source surface was -0.9±0.2%. Our results show that this method is a promising tool for physically-based deformable surface registration. We also used our technique to register ultrasound strain images to free mount histo-pathology images with the goal of correlating cancer with areas of low strain. This was done using relative stiffness values derived from vibroelastography data. We also performed Computed Tomography (CT) and Ultrasound (US) kidney surface registration using this technique.
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Sinasac, Michael John. "Use of 3D deformable models for intraoperative visualization and quantification of cerebral tissue resection." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0026/MQ50663.pdf.
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Tang, Yuxing. "Weakly supervised learning of deformable part models and convolutional neural networks for object detection." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEC062/document.
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Dans cette thèse, nous nous intéressons au problème de la détection d’objets faiblement supervisée. Le but est de reconnaître et de localiser des objets dans les images, n’ayant à notre disposition durant la phase d’apprentissage que des images partiellement annotées au niveau des objets. Pour cela, nous avons proposé deux méthodes basées sur des modèles différents. Pour la première méthode, nous avons proposé une amélioration de l’approche ”Deformable Part-based Models” (DPM) faiblement supervisée, en insistant sur l’importance de la position et de la taille du filtre racine initial spécifique à la classe. Tout d’abord, un ensemble de candidats est calculé, ceux-ci représentant les positions possibles de l’objet pour le filtre racine initial, en se basant sur une mesure générique d’objectness (par region proposals) pour combiner les régions les plus saillantes et potentiellement de bonne qualité. Ensuite, nous avons proposé l’apprentissage du label des classes latentes de chaque candidat comme un problème de classification binaire, en entrainant des classifieurs spécifiques pour chaque catégorie afin de prédire si les candidats sont potentiellement des objets cible ou non. De plus, nous avons amélioré la détection en incorporant l’information contextuelle à partir des scores de classification de l’image. Enfin, nous avons élaboré une procédure de post-traitement permettant d’élargir et de contracter les régions fournies par le DPM afin de les adapter efficacement à la taille de l’objet, augmentant ainsi la précision finale de la détection. Pour la seconde approche, nous avons étudié dans quelle mesure l’information tirée des objets similaires d’un point de vue visuel et sémantique pouvait être utilisée pour transformer un classifieur d’images en détecteur d’objets d’une manière semi-supervisée sur un large ensemble de données, pour lequel seul un sous-ensemble des catégories d’objets est annoté avec des boîtes englobantes nécessaires pour l’apprentissage des détecteurs. Nous avons proposé de transformer des classifieurs d’images basés sur des réseaux convolutionnels profonds (Deep CNN) en détecteurs d’objets en modélisant les différences entre les deux en considérant des catégories disposant à la fois de l’annotation au niveau de l’image globale et l’annotation au niveau des boîtes englobantes. Cette information de différence est ensuite transférée aux catégories sans annotation au niveau des boîtes englobantes, permettant ainsi la conversion de classifieurs d’images en détecteurs d’objets. Nos approches ont été évaluées sur plusieurs jeux de données tels que PASCAL VOC, ImageNet ILSVRC et Microsoft COCO. Ces expérimentations ont démontré que nos approches permettent d’obtenir des résultats comparables à ceux de l’état de l’art et qu’une amélioration significative a pu être obtenue par rapport à des méthodes récentes de détection d’objets faiblement superviséesIn this dissertation we address the problem of weakly supervised object detection, wherein the goal is to recognize and localize objects in weakly-labeled images where object-level annotations are incomplete during training. To this end, we propose two methods which learn two different models for the objects of interest. In our first method, we propose a model enhancing the weakly supervised Deformable Part-based Models (DPMs) by emphasizing the importance of location and size of the initial class-specific root filter. We first compute a candidate pool that represents the potential locations of the object as this root filter estimate, by exploring the generic objectness measurement (region proposals) to combine the most salient regions and “good” region proposals. We then propose learning of the latent class label of each candidate window as a binary classification problem, by training category-specific classifiers used to coarsely classify a candidate window into either a target object or a non-target class. Furthermore, we improve detection by incorporating the contextual information from image classification scores. Finally, we design a flexible enlarging-and-shrinking post-processing procedure to modify the DPMs outputs, which can effectively match the approximate object aspect ratios and further improve final accuracy. Second, we investigate how knowledge about object similarities from both visual and semantic domains can be transferred to adapt an image classifier to an object detector in a semi-supervised setting on a large-scale database, where a subset of object categories are annotated with bounding boxes. We propose to transform deep Convolutional Neural Networks (CNN)-based image-level classifiers into object detectors by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We have evaluated both our approaches extensively on several challenging detection benchmarks, e.g. , PASCAL VOC, ImageNet ILSVRC and Microsoft COCO. Both our approaches compare favorably to the state-of-the-art and show significant improvement over several other recent weakly supervised detection methods
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Lee, M. E. M. "Mathematical models of the carding process." Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249543.
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Carding is an essential pre-spinning process whereby masses of dirty tufted fibres are cleaned, disentangled and refined into a smooth coherent web. Research and development in this `low-technology' industry have hitherto depended on empirical evidence. In collaboration with the School of Textile Industries at the University of Leeds, a mathematical theory has been developed that describes the passage of fibres through the carding machine. The fibre dynamics in the carding machine are posed, modelled and simulated by three distinct physical problems: the journey of a single fibre, the extraction of fibres from a tuft or tufts and many interconnecting, entangled fibres. A description of the life of a single fibre is given as it is transported through the carding machine. Many fibres are sparsely distributed across machine surfaces, therefore interactions with other neighbouring fibres, either hydrodynamically or by frictional contact points, can be neglected. The aerodynamic forces overwhelm the fibre's ability to retain its crimp or natural curvature, and so the fibre is treated as an inextensible string. Two machine topologies are studied in detail, thin annular regions with hooked surfaces and the nip region between two rotating drums. The theoretical simulations suggest that fibres do not transfer between carding surfaces in annular machine geometries. In contrast to current carding theories, which are speculative, a novel explanation is developed for fibre transfer between the rotating drums. The mathematical simulations describe two distinct mechanisms: strong transferral forces between the taker-in and cylinder and a weaker mechanism between cylinder and doffer. Most fibres enter the carding machine connected to and entangled with other fibres. Fibres are teased from their neighbours and in the case where their neighbours form a tuft, which is a cohesive and resistive fibre structure, a model has been developed to understand how a tuft is opened and broken down during the carding process. Hook-fibre-tuft competitions are modelled in detail: a single fibre extracted from a tuft by a hook and diverging hook-entrained tufts with many interconnecting fibres. Consequently, for each scenario once fibres have been completely or partially extracted, estimates can be made as to the degree to which a tuft has been opened-up. Finally, a continuum approach is used to simulate many interconnected, entangled fibre-tuft populations, focusing in particular on their deformations. A novel approach describes this medium by density, velocity, directionality, alignment and entanglement. The materials responds to stress as an isotropic or transversely isotropic medium dependent on the degree of alignment. Additionally, the material's response to stress is a function of the degree of entanglement which we describe by using braid theory. Analytical solutions are found for elongational and shearing flows, and these compare very well with experiments for certain parameter regimes.
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Khan, Irfan. "Direct numerical simulation and analysis of saturated deformable porous media." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34664.
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Existing numerical techniques for modeling saturated deformable porous media are based on
homogenization techniques and thus are incapable of performing micro-mechanical investigations, such as the effect of micro-structure on the deformational characteristics of the media. In this research work, a numerical scheme is developed based on the parallelized hybrid lattice-Boltzmann finite-element method, that is capable of performing micro-mechanical investigations through direct numerical
simulations.
The method has been used to simulate compression of model saturated porous media made of
spheres and cylinders in regular arrangements. Through these simulations it is found that in the limit of small Reynolds number, Capillary number and strain, the deformational behaviour of a real porous media can be recovered through model porous media when the parameters porosity, permeability and bulk compressive modulus are matched between the two media.
This finding motivated research in using model porous geometries to represent more complex
real porous geometries in order to perform investigations of deformation on the latter. An attempt has been made to apply this technique to the complex geometries of ªfeltº, (a fibrous mat used in paper industries). These investigations lead to new understanding on the effect of fiber diameter on the bulk properties of a fibrous media and subsequently on the deformational behaviour of the media. Further the method has been used to investigate the constitutive relationships in deformable porous media.
Particularly the relationship between permeability and porosity during the deformation of the media is investigated. Results show the need of geometry specific investigations.
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Sengupta, Agniva. "Visual tracking of deformable objects with RGB-D camera." Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S069.
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Le suivi d'objets déformable à partir d’informations visuelles à de nombreuses applications dans le domaine de la robotique, de l'animation ou de la simulation. Dans cette thèse, nous proposons de nouvelles approches pour le suivi d'objets rigides et non rigides à l'aide d'une caméra RGB-D. Cette thèse comporte quatre contributions principales. La première contribution est une nouvelle approche de suivi d'objets dans des images RGB-D qui utilise des erreurs basées sur la profondeur et la photométrie pour suivre et localiser des formes complexes en utilisant leur modèle 3D grossier. La seconde contribution porte sur une méthode de suivi d'objets non rigides reposant sur une approche par éléments finis (FEM) pour suivre et caractériser les déformations. La troisième contribution est une approche de suivi de la déformation qui minimise une combinaison d'erreurs géométriques et photométriques tout en utilisant la FEM comme modèle de déformation. Finalement, la quatrième contribution consiste à estimer les propriétés d'élasticité d'un objet analysant ses déformations toujours à l'aide d'une caméra RGB-D. Une fois les paramètres d'élasticité estimés, la même méthodologie peut être réutilisée pour caractériser les forces de contactTracking soft objects using visual information has immense applications in the field of robotics, computer graphics and automation. In this thesis, we propose multiple new approaches for tracking both rigid and non-rigid objects using a RGB-D camera. There are four main contributions of this thesis. The first contribution is a rigid object tracking method which utilizes depth and photometry based errors for tracking complex shapes using their coarse, 3D template. The second contribution is a non-rigid object tracking method which uses co-rotational FEM to track deforming objects by regulating the virtual forces acting on the surface of a physics based model of the object. The third contribution is a deformation tracking approach which minimizes a combination of geometric and photometric error while utilizing co-rotation FEM as the deformation model. The fourth contribution involves estimating the elasticity properties of a deforming object while tracking their deformation using RGB-D camera. Once the elasticity parameters have been estimated, the same methodology can be re-utilized for tracking contact forces on the surface of deforming objects
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CAMPOS, Jamilson Ramos. "Modelos deformáveis de partículas e algoritmos de colisões aplicados à simulação de tecidos." Universidade Federal de Campina Grande, 2006. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1140.
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Este trabalho apresenta um estudo de dois modelos computacionais deformáveis aplicados à simulação de tecidos, ambos modelos de mecânica de partículas fisicamente embasados, contemplando algoritmos, um para cada modelo, para o tratamento de colisões. Estudamos um modelos de malha retangular (clássico e pouco robusto) e um baseado em malha triangular (moderno e robusto) através da implementação, simulações e uma análise qualitativa simples entre os resultados visuais obtidos com ambos. Nenhum destes modelos apresenta relações entre deformações tangenciais e normais e portanto, não geram rugas e/ou dobras espontaneamente. Para torná-los mais realísticos, em nossa implementação, propomos o uso de uma força de acoplamento entre as deformações tangenciais normais.
This work presents a study of two deformable computational models applied to the simulation of cloths, both physically based models of particle mechanics, contemplating one per model, collision treatment algorithms. We study a rectangular grid model (classic and not very robust one) and a triangular mesh based model (modern and robust one) throught implementation, simulations and a simple qualitative analysis between visual results reached with them. Neither of these models presents a relationship with tangent and normal deformations therefore don't build folds and/or wrinkles spontaneously. to turn then more realistic, in our implementation, we propose to make use of a coupling force between tangent and normal deformations.
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Bosman, Julien. "Physically-based 6-DoF nodes deformable models : application to connective tissues simulation and soft-robots control." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10122/document.
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La simulation médicale est un domaine de recherche de plus en plus actif. Malgré les avancées observées ces dernières années, le modèle complet du patient virtuel reste un objectif ambitieux. Il existe encore de nombreuses opportunités de recherche, notamment concernant la modélisation mécanique des conditions aux limites. Jusqu'à présent, la majorité des travaux était consacrée à la simulation d'organes, ces derniers étant généralement simulés seuls. Cette situation pose problème car l'influence des organes environnants sur les conditions aux limites est négligée. Ces interactions peuvent être complexes, impliquant des contacts mais aussi des liaisons mécaniques dues aux tissus conjonctifs. Ainsi, les influences mutuelles entre les structures anatomiques sont souvent simplifiées, diminuant le réalisme des simulations. Cette thèse visé à étudier l'importance des tissus conjonctifs, et plus particulièrement d'une bonne modélisation des conditions aux limites. Dans ce but, le rôle des ligaments lors d'une intervention chirurgicale par laparoscopie a été étudié. Afin d'améliorer le réalisme des simulations, un modèle mécanique dédié aux tissus conjonctifs, basée sur la mécanique des milieux continus et un ensemble de nœuds à 6 degrés de liberté a été développée. En outre, les travaux sur les tissus conjonctifs ont donné lieu à la mise au point d'une méthode de modélisation utilisée dans le cadre des robots déformables. Cette méthode permet un contrôle précis, et temps-réel, d'un bras robotisé déformable. L'utilisation de nœuds orientables a donné lieu à un modèle à nombre de degrés de liberté réduit, permettant de reproduire le comportement de structures plus complexesDespite the promising advances done in medical simulation, the complete virtual patient’s model is yet to come. There are still many avenues for improvements, especially concerning the mechanical modeling of boundary conditions.So far, most of the work has been dedicated to organs simulation, which are generally simulated alone. This raises a real problem as the role of the surrounding organs in boundary conditions is neglected. However, these interactions can be complex, involving contacts but also mechanical links provided by layers of soft tissues known as connective tissues. As a consequence, the mutual influences between the anatomical structures are generally simplified, weakening realism of simulations.This thesis aims at studying the importance of the connective tissues, and especially of a proper modeling of the boundary conditions. To this end, the role of the ligaments during laparoscopic liver surgery has been investigated. In order to enhance the simulations’ realism, a mechanical model dedicated to the connective tissues has been worked out. This has led to the development of a physically-based method relying on material points that can, not only translate, but also rotate themselves. The goal of this model is to enable the simulation of multiple organs linked by complex interactions.In addition, the work on the connective tissues model has been derived to be used in soft robotics. The principle of relying on orientable material points has been used to developed a reduced model that can reproduce the behavior of more complex structures. The objective of this work is to provide the means to control – in real-time – a soft robot made of a deformable arm
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Dendere, Ronald. "Segmentation of candidate bacillus objects in images of Ziehl-Neelsen-stained sputum smears using deformable models." Master's thesis, University of Cape Town, 2009. http://hdl.handle.net/11427/3232.
Full textAbstract:
Includes abstract.Includes bibliographical references (leaves 83-88).
Automated microscopy for the detection of tuberculosis (TB) in sputum smears seeks to address the strain on technicians and to achieve faster diagnosis in order to cope with the rising number of TB cases. Image processing techniques provide a useful alternative to the conventional, manual analysis of sputum smears for diagnosis. In the project described here, the use of parametric and geometric deformable models was explored for segmentation of TB bacilli in images of Ziehl-Neelsen-stained sputum smears for automated TB diagnosis. The goal of segmentation is to produce candidate bacillus objects for input into a classifier.
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Balakrishnan, Sreenath. "A Numerical Elastic Model for Deforming Bat Pinnae." Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/36406.
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In bats, the directivity patterns for reception are shaped by the surface geometry of the
pinnae. Since many bat species are capable of large ear deformations, these beampatterns
can be time-variant. To investigate this time-variance using numerical methods, a digital
model that is capable of representing the pinna geometry during the entire deformation cycle
has been developed.
Due to large deformations and occlusions, some of the surfaces relevant to sound diffraction
may not be visible and the geometry of the entire pinna has to be computed from limited
data. This has been achieved by combining a complete digital model of the pinna in one
position with time-variant sparse sets of three dimensional landmark data. The landmark
positions were estimated using stereo vision methods. A finite element model based on
elasticity was constructed from CT scans of the pinna post mortem. This elastic model
was deformed to provide a good fit to the positions of the landmarks and retain values of
smoothness and surface energy comparable to life. This model was able to handle ratios
of data to degrees of freedom around 1:5000 and still effect life-like deformations with an
acceptable goodness of fit.
Master of Science
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Tuft, David O. "System for collision detection between deformable models built on axis aligned bounding boxes and GPU based culling /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1689.pdf.
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Tuft, David Owen. "System for Collision Detection Between Deformable Models Built on Axis Aligned Bounding Boxes and GPU Based Culling." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/1120.
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Collision detection between deforming models is a difficult problem for collision detection systems to handle. This problem is even more difficult when deformations are unconstrained, objects are in close proximity to one another, and when the entity count is high. We propose a method to perform collision detection between multiple deforming objects with unconstrained deformations that will give good results in close proximities. Currently no systems exist that achieve good performance on both unconstrained triangle level deformations and deformations that preserve edge connectivity. We propose a new system built as a combination of Graphics Processing Unit (GPU) based culling and Axis Aligned Bounding Box (AABB) based culling. Techniques for performing hierarchy-less GPU-based culling are given. We then discuss how and when to switch between GPU-based culling and AABB based techniques.
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Martine, Hagai Mbakize. "Towards automatic modeling of buildings in informal settlements from aerial photographs using deformable active contour models (snakes)." Doctoral thesis, University of Cape Town, 2001. http://hdl.handle.net/11427/4983.
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Bibliography: leaves 177-187.This dissertation presents a novel system for semi-automatic modeling of buildings in informal settlement areas from aerial photographs. The building extraction strategy is developed and implememed with the aim of generatinga a desk top Informal Settlement Geographic lnformation System (ISGIS) using felf developed and available PC-based GIS tools to serve novice users informal settlement areas.
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Langstaff, Meredith Avery. "Mechanical Models of Coontinental Plate Boundaries Fault Slip Rates and Interseismic Stress Rotation Rates." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11345.
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We first describe the methodology for a two-dimensional, elastic deformable microplate modeling approach for continental plate boundaries. Deformable microplate models combine discrete slip on microplate boundaries (faults) with continuous deformation in block interiors. Two idealized models simulating continental collision are presented, one with two microplates and one with four microplates.Earth and Planetary Sciences
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Rodríguez, Eduardo Rafael Llapa. "Segmentação de fronteiras em imagens médicas via contornos deformáveis através do fluxo recursivo do vetor gradiente." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-03022016-161317/.
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Devido à variação na qualidade e ao ruído nas imagens médicas, a aplicação de técnicas tradicionais de segmentação é geralmente ineficiente. Nesse sentido, apresenta-se um novo algoritmo a partir de duas técnicas: o modelo de contornos deformáveis por fluxo do vetor gradiente (GVF deformable contours) e a técnica de espaço de escalas utilizando o processo de difusão. Assim, foi realizada uma revisão bibliográfica dos modelos que trabalham com os contornos deformáveis, os quais foram classificados em modelos paramétricos e geométricos. Entre os modelos paramétricos foi escolhido o modelo de contornos deformáveis por fluxo do vetor gradiente (GVF). Esta aproximação oferece precisão na representação de estruturas biológicas não observada em outros modelos. Desta forma, o algoritmo apresentado mapeia as bordas (edge map) e aperfeiçoa a condução da deformação utilizando uma técnica baseada em operações recursivas. Com este cálculo apoiado no comportamento de espaço de escalas, obtem-se a localização e correção de sub-regiões do edge map que perturbam a deformação. Por outro lado, é incorporada uma nova característica que permite ao algoritmo realizar atividades de classificação. O algoritmo consegue determinar a presença ou ausência de um objeto de interesse utilizando um valor mínimo de deformação. O algoritmo é validado através do tratamento de imagens sintéticas e médicas comparando os resultados com os obtidos no modelo tradicional de contornos deformáveis GVF.Due to the variation of the quality and noise in medical images, the classic image segmentation techniques are usually ineffective. In this work, we present a new algorithm that is composed of two techniques: the gradient vector flow deformable contours (GVF) and the scale-space technique using a diffusion process. A bibliographical revision of the models that work with deformable contours was accomplished, they were classified in parametric and geometric models. Among the parametric models the gradient vector flow deformable contours (GVF) was chosen. This approach offers precision in the representation of biological structures where other models does not. Thus, the algorithm improves the edge map to guide the deformation using recursive operations. With this estimation based on the behavior of the scale-space techniques it is realized, the localization and correction of sub-areas of the edge map that disturb the deformation. On the other hand, it was incorporated a new characteristic that allows the algorithm to accomplish classification activities. That is, the algorithm determines the presence or absence of a target object using a minimal deformation area. Our method was validated on both, simulated images and medical images making a comparison with the traditional GVF deformable contours.
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Weischedel, Clarisse [Verfasser], Max [Akademischer Betreuer] Wardetzky, and Bernd [Akademischer Betreuer] Simeon. "A discrete geometric view on shear-deformable shell models / Clarisse Weischedel. Gutachter: Max Wardetzky ; Bernd Simeon. Betreuer: Max Wardetzky." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://d-nb.info/1042735468/34.
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Pham, Minh Tuan. "Analýza změny objemu hipokampu u pacientů s Alzheimerovou chorobou." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220871.
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Interest in hippocampus increased sharply after his significance in the process of learning and retention of information was published. In particular, considerable interest was in its volume changes and their effect on Alzheimer’s disease. Understanding the structure and function hippocampus would contribute to a more accurate diagnosis of this disease. In this work was created a method of hippocampal segmentation using active contours. With its help, the data composed of both healthy and a diseased patients was segmented and the results were then statistically analyzed using statistical methods such as Kruskal-Walis test, Mann-Whitney test. The level of significance given by results of analysis supports alternative hypothesis that attaches significance of the difference in volume of the hippocampus between studied groups.