Relevant bibliographies by topics / Structure- Motion

Academic literature on the topic 'Structure- Motion'

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Published: 6 September 2023

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Journal articles on the topic "Structure- Motion"

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Backus, B., and B. Caziot. "Motion from structure." Journal of Vision 12, no. 9 (August 10, 2012): 774. http://dx.doi.org/10.1167/12.9.774.

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An, Dong, Tie Jun Qu, and Jian Wen Liang. "Pseudo-Dynamic Test of Brick Masonry under Different Earthquake Motion." Applied Mechanics and Materials 256-259 (December 2012): 2111–16. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.2111.

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For further study on the influence of ground motion on the seismic performance of brick masonry structure, two masonry buildings were designed and constructed according to common structure style in towns and villages. Two full-scale buildings were subjected to different earthquake motion using pseudo-dynamic test. The earthquake motions are artificial earthquake motion and strong motion recording. This paper presents hysteretic behavior and deformation under horizontal seismic action. Crack propagation of masonry structure is analyzed. These tests verify that displacement response of masonry structures under different ground motion is pretty much the same. Tie-column and ring-beam can effectively confined brick wall. The cracks are mostly diagonal cracks caused by shear failure.
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Mitsugami, Ikuhisa. "Bundler: Structure from motion for unordered image collections." Journal of The Institute of Image Information and Television Engineers 65, no. 4 (2011): 479–82. http://dx.doi.org/10.3169/itej.65.479.

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Ding, Yanqiong, Yazhou Xu, and Shuhang Ding. "A Stochastic Earthquake Ground Motion Database and Its Application in Seismic Analysis of an RC Frame-Shear Wall Structure." Buildings 13, no. 7 (June 27, 2023): 1637. http://dx.doi.org/10.3390/buildings13071637.

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A stochastic earthquake ground motion database comprising twelve groups of simulated ground motions was introduced. Ground motions were generated using the stochastic semi-physical model of earthquake ground motions, based on a cluster analysis of 7778 recorded earthquake ground motion. All twelve groups of simulated earthquake ground motions were validated through the probability density evolution method (PDEM) by comparing their time histories and response spectra. As an application of the proposed database, an 18-story reinforced concrete (RC) frame-shear wall structure was analyzed using one group of simulated earthquake ground motions. The probability densities of the top displacement of the structure were estimated using PDEM, highlighting the significant stochasticity of the structural response. The seismic reliability of the structure was assessed by evaluating the extreme value distribution of the story drift angle. The investigations indicate that the proposed stochastic earthquake ground motion database effectively captures the inherent stochasticity of ground motions. Moreover, it contributes to enhancing the efficiency of reliability assessments for structures.
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Bae, Jinseok, Hojun Jang, Cheol-Hui Min, Hyungun Choi, and Young Min Kim. "Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 86–94. http://dx.doi.org/10.1609/aaai.v36i1.19882.

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We present Neural Marionette, an unsupervised approach that discovers the skeletal structure from a dynamic sequence and learns to generate diverse motions that are consistent with the observed motion dynamics. Given a video stream of point cloud observation of an articulated body under arbitrary motion, our approach discovers the unknown low-dimensional skeletal relationship that can effectively represent the movement. Then the discovered structure is utilized to encode the motion priors of dynamic sequences in a latent structure, which can be decoded to the relative joint rotations to represent the full skeletal motion. Our approach works without any prior knowledge of the underlying motion or skeletal structure, and we demonstrate that the discovered structure is even comparable to the hand-labeled ground truth skeleton in representing a 4D sequence of motion. The skeletal structure embeds the general semantics of possible motion space that can generate motions for diverse scenarios. We verify that the learned motion prior is generalizable to the multi-modal sequence generation, interpolation of two poses, and motion retargeting to a different skeletal structure.
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Isaacson, M. "Ice Mass Motions Near an Offshore Structure." Journal of Offshore Mechanics and Arctic Engineering 109, no. 2 (May 1, 1987): 206–10. http://dx.doi.org/10.1115/1.3257011.

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The present paper treats the motions of an ice mass up to the instant of impact with a large fixed offshore structure, and describes a numerical method for predicting these motions taking account of the interaction between the ice mass and structure. In general an ice mass will undergo wave-induced oscillatory motions as well as drift motion. The former are calculated by linear diffraction theory applied to bodies of arbitrary shape so that interaction effects are fully accounted for. The drift motion is calculated by a time-stepping procedure applied to the drift equations of motion which involve zero frequency added masses, drag forces and wave drift forces. As an example of the methods application, results are presented for a typical design situation which illustrate the nature of the hydrodynamic interaction between the ice mass and structure.
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Tsuji, Toshio, Yusuke Ishida, Koji Ito, Mitsuo Nagamachi, and Tatsuo Nishino. "Motor Schema Model Learned by Structural Neural Networks." Journal of Robotics and Mechatronics 2, no. 4 (August 20, 1990): 258–65. http://dx.doi.org/10.20965/jrm.1990.p0258.

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Human beings remember plans concerning typical motions which occur frequently as schema, and by selecting suitable schema depending on conditions, generate muscular motion almost unconsciously. Though a motor schema represents typical motions, it is equipped with superior plan structure taking into consideration the concurrency and seriality of motions as seen in grasping actions and walking motions, and the structure of plans can be acquired by learning. In this paper, a study is made of the modeling of such motor schema with the use of neural networks. For this purpose, the neural network is structured beforehand into the part which generates action sequences in the form containing concurrency (concurrent action generation part) and the part which modifies the action sequences to satisfy constraints which cannot be executed concurrently (constraint representation part). After learning in each part model the neural network can generate motion sequences while taking into consideration the seriality and concurrency of motion by combining the parts at the time of execution. Finally, this model is applied to the formation of typewriting action motor schema, and it is demonsted that generates motion sequences which take into consideration the constraint of the motion system accompanying the execution of motion.
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Turner, Jessica, and Myron L. Braunstein. "Size Constancy in Structure from Motion." Perception 24, no. 10 (October 1995): 1155–64. http://dx.doi.org/10.1068/p241155.

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The relative motions of points in a structure-from-motion display involving parallel projection provide depth information in an object-centered framework: differences in velocity do not reflect differences in distance from an eyepoint. In contrast, size constancy is generally regarded to be a perspective effect, based on the relationship between projected size and distance from an eyepoint. Five subjects judged the relative sizes of objects in structure-from-motion scenes. Although the scenes were displayed without perspective, judged size was related to the simulated separation in depth of the objects. These results suggest that relative depths recovered from object-centered information are incorporated into a viewer-centered framework.
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Wang, Ren Zuo, Shih Hung Chen, Bing Chang Lin, Chao Hsun Huang, and Chung Yue Wang. "Nonlinear Analysis of the Motion Structures." Applied Mechanics and Materials 373-375 (August 2013): 90–94. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.90.

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In this paper, the nonlinear analysis of the motion structures is studied by using the vector form intrinsic finite element (VFIFE, V-5) method. The main object of this research is to develop an internal hinge of two ends of the plane frame element. In this study, the hinge function of frame element is used to compute the nonlinear dynamic responses of the motion structures. A fictitious reversed rigid body motion can be used to separate the rigid body motions and the pure deformations of the frame element. It is not requires any iteration or any parameters for the VFIFE method during computation process. Four examples illustrate the accuracy of the proposed procedures in computing large motions of a flying flexible structure.
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Wang, Hao, Abhilash Somayajula, Jeffrey Falzarano, and Zhitian Xie. "Development of a Blended Time-Domain Program for Predicting the Motions of a Wave Energy Structure." Journal of Marine Science and Engineering 8, no. 1 (December 19, 2019): 1. http://dx.doi.org/10.3390/jmse8010001.

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Traditional linear time-domain analysis is used widely for predicting the motions of floating structures. When it comes to a wave energy structure, which usually is subjected to larger relative (to their geometric dimensions) wave and motion amplitudes, the nonlinear effects become significant. This paper presents the development of an in-house blended time-domain program (SIMDYN). SIMDYN’s “blend” option improves the linear option by accounting for the nonlinearity of important external forces (e.g., Froude-Krylov). In addition, nonlinearity due to large body rotations (i.e., inertia forces) is addressed in motion predictions of wave energy structures. Forced motion analysis reveals the significance of these nonlinear effects. Finally, the model test correlations examine the simulation results from SIMDYN under the blended option, which has seldom been done for a wave energy structure. It turns out that the blended time-domain method has significant potential to improve the accuracy of motion predictions for a wave energy structure.
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Dissertations / Theses on the topic "Structure- Motion"

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Scheffler, Carl. "Articulated structure from motion." Thesis, University of the Western Cape, 2004. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=init_2988_1177923873.

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The structure from motion (SfM) problem is that of determining 3-dimensional (3D) information of a scene from sequences of 2-dimensional (2D) images [59]. This information consists of object shape and motion and relative camera motion. In general, objects may undergo complex non-rigid motion and may be occluded by other objects or themselves. These aspects make the general SfM problem under-constrained and the solution subject to missing or incomplete data.
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Svensson, Fredrik. "Structure from Forward Motion." Thesis, Linköpings universitet, Bildbehandling, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-60136.

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This master thesis investigates the difficulties of constructing a depth map using one low resolution grayscale camera mounted in the front of a car. The goal is to produce a depth map in real-time to assist other algorithms in the safety system of a car. This has been shown to be difficult using the evaluated combination of camera position and choice of algorithms. The main problem is to estimate an accurate optical flow. Another problem is to handle moving objects. The conclusion is that the implementations, mainly triangulation of corresponding points tracked using a Lucas Kanade tracker, provide information of too poor quality to be useful for the safety system of a car.
I detta examensarbete undersöks svårigheterna kring att skapa en djupbild från att endast använda en lågupplöst gråskalekamera monterad framtill i en bil. Målet är att producera en djupbild i realtid som kan nyttjas i andra delar av bilens säkerhetssystem. Detta har visat sig vara svårt att lösa med den undersökta kombinationen av kameraplacering och val av algoritmer. Det huvudsakliga problemet är att räkna ut ett noggrant optiskt flöde. Andra problem härrör från objekt som rör på sig. Slutsatsen är att implementationerna, mestadels triangulering av korresponderande punktpar som följts med hjälp av en Lucas Kanade-följare, ger resultat av för dålig kvalitet för att vara till nytta för bilens säkerhetssystem.
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Wong, Kwan-Yee Kenneth. "Structure and motion from silhouettes." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621379.

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Zucchelli, Marco. "Optical Flow Based Structure from Motion." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3377.

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Orthey, Andreas. "Exploiting structure in humanoid motion planning." Phd thesis, Toulouse, INPT, 2015. http://oatao.univ-toulouse.fr/14685/1/orthey.pdf.

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If humanoid robots should work along with humans and should be able to solve repetitive tasks, we need to enable them with a skill to autonomously plan motions. Motion planning is a longstanding core problem in robotics, and while its algorithmic foundation has been studied in depth, motion planning is still an NP-hard problem lacking efficient solutions. We want to open up a new perspective on the problem by highlighting its structure: the behavior of the robot, the mechanical system of the robot, and the environment of the robot. We will investigate the hypothesis that each structural component can be exploited to create more efficient motion planning algorithms. We present three algorithms exploiting structure, based on geometrical and topological arguments: first, we exploit the behavior of a walking robot by studying the feasibility of footstep transitions. The resulting algorithm is able to plan footsteps avoiding up to 60 objects on a 6 square meters planar surface. Second, we exploit the mechanical system of a humanoid robot by studying the linear linkage structures of its arms and legs. We introduce the concept of an irreducible motion, which is a completeness-preserving dimensionality reduction technique. The resulting algorithm is able to find motions in narrow environments, where previous sampling-based methods could not be applied. Third, we exploit the environment by reasoning about the topological structure of contact transitions. We show that analyzing the environment is an efficient method to precompute relevant information for efficient motion planning. Based on those results, we come to the conclusion that exploiting structure is an essential component of efficient motion planning. It follows that any humanoid robot, who wants to act efficiently in the real world, needs to be able to understand and to exploit structure.
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Hedborg, Johan. "Motion and Structure Estimation From Video." Doctoral thesis, Linköpings universitet, Datorseende, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-76904.

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Digital camera equipped cell phones were introduced in Japan in 2001, they quickly became popular and by 2003 outsold the entire stand-alone digital camera market. In 2010 sales passed one billion units and the market is still growing. Another trend is the rising popularity of smartphones which has led to a rapid development of the processing power on a phone, and many units sold today bear close resemblance to a personal computer. The combination of a powerful processor and a camera which is easily carried in your pocket, opens up a large eld of interesting computer vision applications. The core contribution of this thesis is the development of methods that allow an imaging device such as the cell phone camera to estimates its own motion and to capture the observed scene structure. One of the main focuses of this thesis is real-time performance, where a real-time constraint does not only result in shorter processing times, but also allows for user interaction. In computer vision, structure from motion refers to the process of estimating camera motion and 3D structure by exploring the motion in the image plane caused by the moving camera. This thesis presents several methods for estimating camera motion. Given the assumption that a set of images has known camera poses associated to them, we train a system to solve the camera pose very fast for a new image. For the cases where no a priory information is available a fast minimal case solver is developed. The solver uses ve points in two camera views to estimate the cameras relative position and orientation. This type of minimal case solver is usually used within a RANSAC framework. In order to increase accuracy and performance a renement to the random sampling strategy of RANSAC is proposed. It is shown that the new scheme doubles the performance for the ve point solver used on video data. For larger systems of cameras a new Bundle Adjustment method is developed which are able to handle video from cell phones. Demands for reduction in size, power consumption and price has led to a redesign of the image sensor. As a consequence the sensors have changed from a global shutter to a rolling shutter, where a rolling shutter image is acquired row by row. Classical structure from motion methods are modeled on the assumption of a global shutter and a rolling shutter can severely degrade their performance. One of the main contributions of this thesis is a new Bundle Adjustment method for cameras with a rolling shutter. The method accurately models the camera motion during image exposure with an interpolation scheme for both position and orientation. The developed methods are not restricted to cellphones only, but is rather applicable to any type of mobile platform that is equipped with cameras, such as a autonomous car or a robot. The domestic robot comes in many  avors, everything from vacuum cleaners to service and pet robots. A robot equipped with a camera that is capable of estimating its own motion while sensing its environment, like the human eye, can provide an eective means of navigation for the robot. Many of the presented methods are well suited of robots, where low latency and real-time constraints are crucial in order to allow them to interact with their environment.
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Örjehag, Erik. "Unsupervised Learning for Structure from Motion." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173731.

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Perception of depth, ego-motion and robust keypoints is critical for SLAM andstructure from motion applications. Neural networks have achieved great perfor-mance in perception tasks in recent years. But collecting labeled data for super-vised training is labor intensive and costly. This thesis explores recent methodsin unsupervised training of neural networks that can predict depth, ego-motion,keypoints and do geometric consensus maximization. The benefit of unsuper-vised training is that the networks can learn from raw data collected from thecamera sensor, instead of labeled data. The thesis focuses on training on imagesfrom a monocular camera, where no stereo or LIDAR data is available. The exper-iments compare different techniques for depth and ego-motion prediction fromprevious research, and shows how the techniques can be combined successfully.A keypoint prediction network is evaluated and its performance is comparedwith the ORB detector provided by OpenCV. A geometric consensus network isalso implemented and its performance is compared with the RANSAC algorithmin OpenCV. The consensus maximization network is trained on the output of thekeypoint prediction network. For future work it is suggested that all networkscould be combined and trained jointly to reach a better overall performance. Theresults show (1) which techniques in unsupervised depth prediction are most ef-fective, (2) that the keypoint predicting network outperformed the ORB detector,and (3) that the consensus maximization network was able to classify outlierswith comparable performance to the RANSAC algorithm of OpenCV.
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Hakl, Henri. "Structure-from-motion for enclosed environments." Thesis, Link to the online version, 2007. http://hdl.handle.net/10019.1/1195.

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Rautenbach, Pieter Albertus. "Facial Feature Reconstruction using Structure from Motion." Thesis, Link to the online version, 2005. http://hdl.handle.net/10019/1340.

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Ji, Hui. "A holistic approach to structure from motion." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3807.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Books on the topic "Structure- Motion"

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Weng, Juyang. Motion and structure fromimage sequences. Berlin: Springer-Verlag, 1993.

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Weng, Juyang, Thomas S. Huang, and Narendra Ahuja. Motion and Structure from Image Sequences. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77643-4.

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Carrivick, Jonathan L., Mark W. Smith, and Duncan J. Quincey. Structure from Motion in the Geosciences. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118895818.

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Weng, Juyang. Motion and structure from image sequences. Berlin: Springer-Verlag, 1993.

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Weng, Juyang. Motion and Structure from Image Sequences. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993.

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Klimontovich, Yu L. Turbulent Motion and the Structure of Chaos. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3426-2.

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Nir, Ben-Tal, ed. Introduction to proteins: Structure, function, and motion. Boca Raton, FL: CRC Press, 2011.

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Kara, Rogers, ed. Bone and muscle: Structure, force, and motion. New York, NY: Britannica Educational Pub. in association with Rosen Education Services, 2010.

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Rogers, Kara. Bone and Muscle: Structure, Force, and Motion. Chicago: Britannica Educational Pub., 2011.

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McLauchlan, Philip F. Accurate mosaicing using structure from motion methods. Guildford: Department of Electronic and Electrical Engineering, University of Surrey, 1999.

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Book chapters on the topic "Structure- Motion"

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Szeliski, Richard. "Structure from motion." In Texts in Computer Science, 303–34. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-935-0_7.

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Quan, Long. "Structure from Motion." In Image-Based Modeling, 85–118. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6679-7_5.

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Minkin, Louisa, Thomas Allison, and Andrew Meirion Jones. "Structure from motion." In Diffracting Digital Images, 50–64. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003042129-4.

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Pinard, Clément, Laure Chevalley, Antoine Manzanera, and David Filliat. "Learning Structure-from-Motion from Motion." In Lecture Notes in Computer Science, 363–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11015-4_27.

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Burger, Wilhelm, and Bir Bhanu. "Reasoning about Structure and Motion." In Qualitative Motion Understanding, 107–28. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3566-9_6.

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Calvert, J. R. "Foam in Motion." In Foams: Physics, Chemistry and Structure, 27–37. London: Springer London, 1989. http://dx.doi.org/10.1007/978-1-4471-3807-5_3.

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Klimontovich, Yu L. "Brownian Motion." In Turbulent Motion and the Structure of Chaos, 220–66. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3426-2_5.

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Nicosevici, Tudor, and Rafael Garcia. "Direct Structure from Motion." In Springer Tracts in Advanced Robotics, 39–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36418-1_3.

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Fang, Tian, and Long Quan. "Resampling Structure from Motion." In Computer Vision – ECCV 2010, 1–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15552-9_1.

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Belongie, Serge, and Josh Wills. "Structure from Periodic Motion." In Spatial Coherence for Visual Motion Analysis, 16–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11676959_2.

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Conference papers on the topic "Structure- Motion"

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Lee, Du-Ho, Youn-Ju Jeong, Young-Jun You, and Min-Su Park. "Structural Performance of the Optimum Floating Structure for Reduced Motion." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10697.

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In order to design a reliable floating structure, the hydrodynamic motion and structural performance under wave loadings should be reduced with the effects of wave-induced hydraulic pressure acting on the floating structure. In this study, analytical studies were carried out for optimum type to reduce the hydrodynamic motion and pressure of concrete floating structure. The optimum floating structure is combined with pontoon-type and hybrid-type floating structures, called combination-type floating structure. In order to verify reducing motion and improving structural performance of combination-type floating structure, analytical studies were carried out for the floating structures. After hydrodynamic analysis, the six degree motions of structure are investigated for fifth periods in shallow water. The hydrodynamic motions of combination-type are lower than other type of floating structures. It meant that the combination-type floating structure can be very efficient to reduce the wave forces acting on structures and be slightly influenced by the incident waves. In addition, to evaluate structural performance of floating structures under the critical wave load that presents maximum motion of floating structure. As the results of this study, the combination-type floating structure identified reducing hydrodynamic motion and excellent structural performance than other floating structures. However, high concentrated stress occurred at the edge of the bottom slab of the bow and stern parts where cylinder wall was connected to the bottom slab. Therefore, some alternatives which can be easily obtained from a simply modification of structural details are proposed to overcome these problems.
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Roy, S., and I. J. Cox. "Motion without structure." In Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.546120.

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Zappella, Luca, Alessio Del Bue, Xavier Llado, and Joaquim Salvi. "Simultaneous motion segmentation and Structure from Motion." In 2011 IEEE Workshop on Applications of Computer Vision (WACV). IEEE, 2011. http://dx.doi.org/10.1109/wacv.2011.5711570.

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Klingner, Bryan, David Martin, and James Roseborough. "Street View Motion-from-Structure-from-Motion." In 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, 2013. http://dx.doi.org/10.1109/iccv.2013.122.

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Schonberger, Johannes L., and Jan-Michael Frahm. "Structure-from-Motion Revisited." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.445.

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Bao, Sid Yingze, and Silvio Savarese. "Semantic structure from motion." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995462.

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Abrams, Austin, Ian Schillebeeckx, and Robert Pless. "Structure from shadow motion." In 2014 IEEE International Conference on Computational Photography (ICCP). IEEE, 2014. http://dx.doi.org/10.1109/iccphot.2014.6831802.

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Soatto, S., and P. Perona. "Reducing "structure from motion"." In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/cvpr.1996.517167.

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Forsyth, D. A., S. Ioffe, and J. Haddon. "Bayesian structure from motion." In Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE, 1999. http://dx.doi.org/10.1109/iccv.1999.791288.

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Zheng, Enliang, and Changchang Wu. "Structure from Motion Using Structure-Less Resection." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.240.

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Reports on the topic "Structure- Motion"

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Thompson, William B. Structure from Motion. Fort Belvoir, VA: Defense Technical Information Center, December 1985. http://dx.doi.org/10.21236/ada175059.

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Ritchie, Elizabeth A. Tropical Cyclone Structure and Motion. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada610205.

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Ritchie, Elizabeth A., R. L. Elsberry, and P. A. Harr. Tropical Cyclone Structure and Motion. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada630661.

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Ritchie, Elizabeth A. Tropical Cyclone Structure and Motion. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada625681.

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Todd, James T. Visual Perception of Structure from Motion. Fort Belvoir, VA: Defense Technical Information Center, April 1992. http://dx.doi.org/10.21236/ada253235.

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Harr, Patrick A. Tropical Cyclone Formation/Structure/Motion Studies. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada548344.

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Lange, S., and J. Boike. Aerial survey and structure from motion. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2019. http://dx.doi.org/10.4095/321049.

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Todd, James T. Visual Perception of Structure from Motion. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada216416.

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Alon, Jonathan, and Stan Sclaroff. Recursive Estimation of Motion and Planer Structure. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada451473.

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Klein, A. Theoretical research in nuclear structure and nuclear collective motion. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/7142155.

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You might also be interested in the extended bibliographies on the topic 'Structure- Motion' for particular source types:
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