Relevant bibliographies by topics / Deformable models

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Deformable models'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Deformable models"

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Terzopoulos, Demetri, and Kurt Fleischer. "Deformable models." Visual Computer 4, no. 6 (November 1988): 306–31. http://dx.doi.org/10.1007/bf01908877.

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Terzopoulos, Demetri, John Platt, Alan Barr, and Kurt Fleischer. "Elastically deformable models." ACM SIGGRAPH Computer Graphics 21, no. 4 (August 1987): 205–14. http://dx.doi.org/10.1145/37402.37427.

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DeCarlo, D., and D. Metaxas. "Blended deformable models." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 4 (April 1996): 443–48. http://dx.doi.org/10.1109/34.491626.

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Metaxas, D. N., and I. A. Kakadiaris. "Elastically adaptive deformable models." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 10 (October 2002): 1310–21. http://dx.doi.org/10.1109/tpami.2002.1039203.

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Güdükbay, Uğur, and Bülent Özgüç. "Animation of deformable models." Computer-Aided Design 26, no. 12 (December 1994): 868–75. http://dx.doi.org/10.1016/0010-4485(94)90051-5.

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Jain, Anil K., Yu Zhong, and Marie-Pierre Dubuisson-Jolly. "Deformable template models: A review." Signal Processing 71, no. 2 (December 1998): 109–29. http://dx.doi.org/10.1016/s0165-1684(98)00139-x.

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Terzopoulos, Demetri, John Platt, and Kurt Fleischer. "Heating and melting deformable models." Journal of Visualization and Computer Animation 2, no. 2 (April 1991): 68–73. http://dx.doi.org/10.1002/vis.4340020208.

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PUJOL, ORIOL, and PETIA RADEVA. "TEXTURE SEGMENTATION BY STATISTICAL DEFORMABLE MODELS." International Journal of Image and Graphics 04, no. 03 (July 2004): 433–52. http://dx.doi.org/10.1142/s021946780400149x.

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Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model.
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Rougon, Nicolas. "Directional adaptive deformable models for segmentation." Journal of Electronic Imaging 7, no. 1 (January 1, 1998): 231. http://dx.doi.org/10.1117/1.482641.

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Hirabayashi, Manato, Shinpei Kato, Masato Edahiro, Kazuya Takeda, and Seiichi Mita. "Accelerated Deformable Part Models on GPUs." IEEE Transactions on Parallel and Distributed Systems 27, no. 6 (June 1, 2016): 1589–602. http://dx.doi.org/10.1109/tpds.2015.2453962.

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Dissertations / Theses on the topic "Deformable models"

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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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Books on the topic "Deformable models"

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Suri, Jasjit S., and Aly A. Farag. Deformable Models. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68343-0.

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Farag, Aly A., and Jasjit S. Suri. Deformable Models. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68413-0.

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Metaxas, Dimitris N. Physics-Based Deformable Models. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6335-8.

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Cai, Jianping, Feng Lin, and Hock Soon Seah. Graphical Simulation of Deformable Models. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51031-6.

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McInerney, Timothy John. Topologically adaptable deformable models for medical image analysis. Toronto: University of Toronto, Dept. of Computer Science, 1997.

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McInerney, Timothy John. Finite element techniques for fitting deformable models to 3D data. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1993.

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McInerney, Timothy John. Finite element techniques for fitting deformable models to 3D data. Toronto: University of Toronto, Dept. of Computer Science, 1992.

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Williams, Christopher K. I. Combining deformable models and neural networks for handpainted digit recognition. Toronto: University of Toronto, Dept. of Computer Science, 1994.

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Physics-based deformable models: Applications to computer vision, graphics, and medical imaging. Boston: Kluwer Academic, 1997.

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Metaxas, Dimitris N. Physics-Based Deformable Models: Applications to Computer Vision, Graphics and Medical Imaging. Boston, MA: Springer US, 1997.

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Book chapters on the topic "Deformable models"

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Suri, Jasjit S., Rodrigo L. S. Silva, Paulo S. S. Rodrigues, Gilson A. Giraldi, Antonio A. F. Oliveira, Edilberto Strauss, and Walter Jiménez. "T-Surfaces Framework For Offset Generation And Semiautomatic 3d Segmentation." In Deformable Models, 1–29. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68343-0_1.

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Fenster, Aaron, Mingyue Ding, and Hanif Ladak. "Deformable Model-Based Segmentation Of The Prostate From Ultrasound Images." In Deformable Models, 325–69. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68343-0_10.

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Zhu, Wanlin, Tianzi Jiang, and Xiaobo Li. "Segmentation Of Brain Mr Images Using J-Divergence Based Active Contour Models." In Deformable Models, 371–91. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68343-0_11.

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Mohamed, Ashraf, and Christos Davatzikos. "Morphometric Analysis Of Normal And Pathologic Brain Structure Via High-Dimensional Shape Transformations." In Deformable Models, 393–445. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68343-0_12.

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Cremers, Daniel, and Mikael Rousson. "Efficient Kernel Density Estimation Of Shape And Intensity Priors For Level Set Segmentation." In Deformable Models, 447–60. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68343-0_13.

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Casanova, Manuel F., H. Abd El Munim, Aly A. Farag, N. Youssry El-Zehiry, and Rachid Fahmi. "Volumetric Mri Analysis Of Dyslexic Subjects Using A Level Set Framework." In Deformable Models, 461–92. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68343-0_14.

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Tustison, Nicholas J., and Amir A. Amini. "Analysis Of 4-D Cardiac Mr Data With Nurbs Deformable Models: Temporal Fitting Strategy And Nonrigid Registration." In Deformable Models, 493–534. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68343-0_15.

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Farag, Aly A., Rachid Fahmi, Manuel F. Casanova, Alaa E. Abdel-Hakim, Hossam Abd El-Munim, and Ayman El-Baz. "Robust Neuroimaging-Based Classification Techniques Of Autistic Vs. Typically Developing Brain." In Deformable Models, 535–66. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68343-0_16.

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Das, Bipul, and Swapna Banerjee. "Parametric Contour Model In Medical Image Segmentation." In Deformable Models, 31–74. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68343-0_2.

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Yang, Lin, and David J. Foran. "Deformable Models And Their Application In Segmentation Of Imaged Pathology Specimens." In Deformable Models, 75–94. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68343-0_3.

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Conference papers on the topic "Deformable models"

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Terzopoulos, Demetri, John Platt, Alan Barr, and Kurt Fleischer. "Elastically deformable models." In the 14th annual conference. New York, New York, USA: ACM Press, 1987. http://dx.doi.org/10.1145/37401.37427.

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DeCarlo and Metaxas. "Blended deformable models." In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE Comput. Soc. Press, 1994. http://dx.doi.org/10.1109/cvpr.1994.323883.

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Green, William D. K. "Spline-based deformable models." In SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Robert A. Melter, Angela Y. Wu, Fred L. Bookstein, and William D. K. Green. SPIE, 1995. http://dx.doi.org/10.1117/12.216421.

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"FACIAL EXPRESSION RECOGNITION USING LOG-EUCLIDEAN STATISTICAL SHAPE MODELS." In Special Session on Shape Analysis and Deformable Modeling. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003867503510359.

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Trulls, Eduard, Stavros Tsogkas, Iasonas Kokkinos, Alberto Sanfeliu, and Francesc Moreno-Noguer. "Segmentation-Aware Deformable Part Models." In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.29.

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Whitaker, Ross T. "Volumetric deformable models: active blobs." In Visualization in Biomedical Computing 1994, edited by Richard A. Robb. SPIE, 1994. http://dx.doi.org/10.1117/12.185173.

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Songsri-in, Kritaphat, George Trigeorgis, and Stefanos Zafeiriou. "Deep and Deformable: Convolutional Mixtures of Deformable Part-Based Models." In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). IEEE, 2018. http://dx.doi.org/10.1109/fg.2018.00040.

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Boussaid, Haithem, Iasonas Kokkinos, and Nikos Paragios. "Discriminative learning of deformable contour models." In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014). IEEE, 2014. http://dx.doi.org/10.1109/isbi.2014.6867948.

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Huang, Zhuan Q., and Zhuhan Jiang. "Deformable object tracking with statistical models." In 2008 2nd International Conference on Signal Processing and Communication Systems (ICSPCS 2008). IEEE, 2008. http://dx.doi.org/10.1109/icspcs.2008.4813679.

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Xu, Weiwei, Jun Wang, KangKang Yin, Kun Zhou, Michiel van de Panne, Falai Chen, and Baining Guo. "Joint-aware manipulation of deformable models." In ACM SIGGRAPH 2009 papers. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1576246.1531341.

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Reports on the topic "Deformable models"

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Sethi, Saratendu, and Stan Sclaroff. Combinations of Non-Rigid Deformable Appearance Models. Fort Belvoir, VA: Defense Technical Information Center, August 1999. http://dx.doi.org/10.21236/ada366984.

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Martinez, Mario J., and Charles Michael Stone. Considerations for developing models of multiphase flow in deformable porous media. Office of Scientific and Technical Information (OSTI), September 2008. http://dx.doi.org/10.2172/940539.

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Lesieutre, George A., and Jeffery L. Kauffman. Damping Models for Shear-Deformable Beam with Applications to Spacecraft Wiring Harness. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada613035.

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Wray, W. O., and T. Aida. Deformable human body model development. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/672307.

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Bodie, Mark, Michael Parker, Alexander Stott, and Bruce Elder. Snow-covered obstacles’ effect on vehicle mobility. Engineer Research and Development Center (U.S.), November 2020. http://dx.doi.org/10.21079/11681/38839.

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Abstract:
The Mobility in Complex Environments project used unmanned aerial systems (UAS) to identify obstacles and to provide path planning in forward operational locations. The UAS were equipped with remote-sensing devices, such as photogrammetry and lidar, to identify obstacles. The path-planning algorithms incorporated the detected obstacles to then identify the fastest and safest vehicle routes. Future algorithms should incorporate vehicle characteristics as each type of vehicle will perform differently over a given obstacle, resulting in distinctive optimal paths. This study explored the effect of snow-covered obstacles on dynamic vehicle response. Vehicle tests used an instrumented HMMWV (high mobility multipurpose wheeled vehicle) driven over obstacles with and without snow cover. Tests showed a 45% reduction in normal force variation and a 43% reduction in body acceleration associated with a 14.5 cm snow cover. To predict vehicle body acceleration and normal force response, we developed two quarter-car models: rigid terrain and deformable snow terrain quarter-car models. The simple quarter models provided reasonable agreement with the vehicle test data. We also used the models to analyze the effects of vehicle parameters, such as ground pressure, to understand the effect of snow cover on vehicle response.
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Liu, Lifeng, and Stan Sclaroff. Region Segmentation via Deformable Model-Guided Split and Merge. Fort Belvoir, VA: Defense Technical Information Center, April 2001. http://dx.doi.org/10.21236/ada451541.

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Liu, Lifeng, and Stan Sclaroff. Deformable Shape Detection and Description via Model-Based Region Grouping. Fort Belvoir, VA: Defense Technical Information Center, August 1999. http://dx.doi.org/10.21236/ada367013.

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Weston, A. Model for high rate gas flows in deformable and reactive porous beds. Office of Scientific and Technical Information (OSTI), January 1985. http://dx.doi.org/10.2172/6217714.

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Reid, Alexander. Compaction-Based Deformable Terrain Model as an Interface for Real-Time Vehicle Dynamics Simulations. Fort Belvoir, VA: Defense Technical Information Center, April 2013. http://dx.doi.org/10.21236/ada573959.

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