Academic literature on the topic 'Vision, Monocular'
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Journal articles on the topic "Vision, Monocular"
Coday, Mary P., Michael A. Warner, Kurt V. Jahrling, and Peter A. D. Rubin. "Acquired Monocular Vision." Ophthalmic Plastic and Reconstructive Surgery 18, no. 1 (January 2002): 56–63. http://dx.doi.org/10.1097/00002341-200201000-00009.
Full textHOLM, EJLER. "NYSTAGMUS IN MONOCULAR VISION." Acta Ophthalmologica 5, no. 1-3 (May 27, 2009): 387–99. http://dx.doi.org/10.1111/j.1755-3768.1927.tb01020.x.
Full textUozato, Hiroshi. "Binocular and Monocular Vision." JAPANESE ORTHOPTIC JOURNAL 35 (2006): 61–66. http://dx.doi.org/10.4263/jorthoptic.35.61.
Full textKraut, Joel A., and Veronica Lopez-Fernandez. "Adaptation to Monocular Vision." International Ophthalmology Clinics 42, no. 3 (2002): 203–13. http://dx.doi.org/10.1097/00004397-200207000-00021.
Full textDickmanns, Ernst Dieter, and Volker Graefe. "Dynamic monocular machine vision." Machine Vision and Applications 1, no. 4 (December 1988): 223–40. http://dx.doi.org/10.1007/bf01212361.
Full textIhrig, Carolyn, and Daniel P. Schaefer. "Acquired Monocular Vision Rehabilitation program." Journal of Rehabilitation Research and Development 44, no. 4 (2007): 593. http://dx.doi.org/10.1682/jrrd.2006.06.0071.
Full textErkelens, C. J., and R. van Ee. "Monocular symmetry in binocular vision." Journal of Vision 7, no. 4 (March 1, 2007): 5. http://dx.doi.org/10.1167/7.4.5.
Full textGuerrero, J. J., and C. Sagüés. "Navigation from Uncalibrated Monocular Vision." IFAC Proceedings Volumes 31, no. 3 (March 1998): 351–56. http://dx.doi.org/10.1016/s1474-6670(17)44110-3.
Full textAbel, Sharon M., and Christine Tikuisis. "Sound localization with monocular vision." Applied Acoustics 66, no. 8 (August 2005): 932–44. http://dx.doi.org/10.1016/j.apacoust.2004.11.011.
Full textRamachandran, V. S., S. Cobb, and L. Levi. "Monocular double vision in strabismus." NeuroReport 5, no. 12 (July 1994): 1418. http://dx.doi.org/10.1097/00001756-199407000-00001.
Full textDissertations / Theses on the topic "Vision, Monocular"
Jama, Michal. "Monocular vision based localization and mapping." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/8561.
Full textDepartment of Electrical and Computer Engineering
Balasubramaniam Natarajan
Dale E. Schinstock
In this dissertation, two applications related to vision-based localization and mapping are considered: (1) improving navigation system based satellite location estimates by using on-board camera images, and (2) deriving position information from video stream and using it to aid an auto-pilot of an unmanned aerial vehicle (UAV). In the first part of this dissertation, a method for analyzing a minimization process called bundle adjustment (BA) used in stereo imagery based 3D terrain reconstruction to refine estimates of camera poses (positions and orientations) is presented. In particular, imagery obtained with pushbroom cameras is of interest. This work proposes a method to identify cases in which BA does not work as intended, i.e., the cases in which the pose estimates returned by the BA are not more accurate than estimates provided by a satellite navigation systems due to the existence of degrees of freedom (DOF) in BA. Use of inaccurate pose estimates causes warping and scaling effects in the reconstructed terrain and prevents the terrain from being used in scientific analysis. Main contributions of this part of work include: 1) formulation of a method for detecting DOF in the BA; and 2) identifying that two camera geometries commonly used to obtain stereo imagery have DOF. Also, this part presents results demonstrating that avoidance of the DOF can give significant accuracy gains in aerial imagery. The second part of this dissertation proposes a vision based system for UAV navigation. This is a monocular vision based simultaneous localization and mapping (SLAM) system, which measures the position and orientation of the camera and builds a map of the environment using a video-stream from a single camera. This is different from common SLAM solutions that use sensors that measure depth, like LIDAR, stereoscopic cameras or depth cameras. The SLAM solution was built by significantly modifying and extending a recent open-source SLAM solution that is fundamentally different from a traditional approach to solving SLAM problem. The modifications made are those needed to provide the position measurements necessary for the navigation solution on a UAV while simultaneously building the map, all while maintaining control of the UAV. The main contributions of this part include: 1) extension of the map building algorithm to enable it to be used realistically while controlling a UAV and simultaneously building the map; 2) improved performance of the SLAM algorithm for lower camera frame rates; and 3) the first known demonstration of a monocular SLAM algorithm successfully controlling a UAV while simultaneously building the map. This work demonstrates that a fully autonomous UAV that uses monocular vision for navigation is feasible, and can be effective in Global Positioning System denied environments.
Cheda, Diego. "Monocular Depth Cues in Computer Vision Applications." Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/121644.
Full textDepth perception is a key aspect of human vision. It is a routine and essential visual task that the human do effortlessly in many daily activities. This has often been associated with stereo vision, but humans have an amazing ability to perceive depth relations even from a single image by using several monocular cues. In the computer vision field, if image depth information were available, many tasks could be posed from a different perspective for the sake of higher performance and robustness. Nevertheless, given a single image, this possibility is usually discarded, since obtaining depth information has frequently been performed by three-dimensional reconstruction techniques, requiring two or more images of the same scene taken from different viewpoints. Recently, some proposals have shown the feasibility of computing depth information from single images. In essence, the idea is to take advantage of a priori knowledge of the acquisition conditions and the observed scene to estimate depth from monocular pictorial cues. These approaches try to precisely estimate the scene depth maps by employing computationally demanding techniques. However, to assist many computer vision algorithms, it is not really necessary computing a costly and detailed depth map of the image. Indeed, just a rough depth description can be very valuable in many problems. In this thesis, we have demonstrated how coarse depth information can be integrated in different tasks following holistic and alternative strategies to obtain more precise and robustness results. In that sense, we have proposed a simple, but reliable enough technique, whereby image scene regions are categorized into discrete depth ranges to build a coarse depth map. Based on this representation, we have explored the potential usefulness of our method in three application domains from novel viewpoints: camera rotation parameters estimation, background estimation and pedestrian candidate generation. In the first case, we have computed camera rotation mounted in a moving vehicle from two novels methods that identify distant elements in the image, where the translation component of the image flow field is negligible. In background estimation, we have proposed a novel method to reconstruct the background by penalizing close regions in a cost function, which integrates color, motion, and depth terms. Finally, we have benefited of geometric and depth information available on single images for pedestrian candidate generation to significantly reduce the number of generated windows to be further processed by a pedestrian classifier. In all cases, results have shown that our depth-based approaches contribute to better performances.
Veldman, Kyle John. "Monocular vision for collision avoidance in vehicles." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101478.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (page 21).
An experimental study facilitated by Ford Global Technologies, Inc. on the potential substitution of stereovision systems in car automation with monocular vision systems. The monocular system pairs a camera and passive lens with an active lens. Most active lenses require linear actuating systems to adjust the optical parameters of the system but this experiment employed an Optotune focus tunable lens adjusted by a Lorentz actuator for a much more reliable system. Tests were conducted in a lab environment to capture images of environmental objects at different distances from the system, pass those images through an image processing algorithm operating a high-pass filter to separate in-focus aspects of the image from out-of focus ones. Although the system is in the early phases of testing, monocular vision shows the ability to replace stereovision system. However, additional testing must be done to acclimate the apparatus to environmental factors, minimize the processing speed, and redesign the system for portability.
by Kyle John Veldman.
S.B.
Ng, Matthew James. "Corridor Navigation for Monocular Vision Mobile Robots." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1856.
Full textPereira, Fabio Irigon. "High precision monocular visual odometry." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/183233.
Full textRecovering three-dimensional information from bi-dimensional images is an important problem in computer vision that finds several applications in our society. Robotics, entertainment industry, medical diagnose and prosthesis, and even interplanetary exploration benefit from vision based 3D estimation. The problem can be divided in two interdependent operations: estimating the camera position and orientation when each image was produced, and estimating the 3D scene structure. This work focuses on computer vision techniques, used to estimate the trajectory of a vehicle equipped camera, a problem known as visual odometry. In order to provide an objective measure of estimation efficiency and to compare the achieved results to the state-of-the-art works in visual odometry a high precision popular dataset was selected and used. In the course of this work new techniques for image feature tracking, camera pose estimation, point 3D position calculation and scale recovery are proposed. The achieved results outperform the best ranked results in the popular chosen dataset.
Goroshin, Rostislav. "Obstacle detection using a monocular camera." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24697.
Full textBenoit, Stephen M. "Monocular optical flow for real-time vision systems." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23862.
Full text李宏釗 and Wan-chiu Li. "Localization of a mobile robot by monocular vision." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226371.
Full textMalan, Daniel Francois. "3D tracking between satellites using monocular computer vision." Thesis, Stellenbosch : University of Stellenbosch, 2005. http://hdl.handle.net/10019.1/3081.
Full textVisually estimating three-dimensional position, orientation and motion, between an observer and a target, is an important problem in computer vision. Solutions which compute threedimensional movement from two-dimensional intensity images, usually rely on stereoscopic vision. Some research has also been done in systems utilising a single (monocular) camera. This thesis investigates methods for estimating position and pose from monocular image sequences. The intended future application is of visual tracking between satellites flying in close formation. The ideas explored in this thesis build on methods developed for use in camera calibration, and structure from motion (SfM). All these methods rely heavily on the use of different variations of the Kalman Filter. After describing the problem from a mathematical perspective we develop different approaches to solving the estimation problem. The different approaches are successfully tested on simulated as well as real-world image sequences, and their performance analysed.
Li, Wan-chiu. "Localization of a mobile robot by monocular vision /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23765896.
Full textBooks on the topic "Vision, Monocular"
Lepetit, Vincent. Monocular model-based 3D tracking of rigid objects. Boston, MA: NOW Publishers, 2005.
Find full textSalzmann, Mathieu. Deformable surface 3D reconstruction from monocular images. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.
Find full textṢafadī, Khalīl ibn Aybak. al- Shuʻūr bi-al-ʻūr. ʻAmmān, al-Urdun: Dār ʻĀmmār, 1988.
Find full textBlind, Royal National Institute for the. Sight in one eye only (monocular vision) and people with learning difficulties. London: R.N.I.B., 2002.
Find full textSchaeren, Peter. Real-time 3-D scene acquisition by monocular motion induced stero. Konstanz: Hartung-Gorre, 1994.
Find full textVerghese, Gilbert. Perspective alignment back-projection for real-time monocular three-dimensional model-based computer vision. Toronto: Dept. of Computer Science, University of Toronto, 1995.
Find full textBrady, Frank B. A singular view: The art of seeing with one eye. 5th ed. Annapolis, Md: F.B. Brady, 1994.
Find full textBrady, Frank B. A singular view: The art of seeing with one eye. Toronto: Edgemore Enterprises, 1992.
Find full textOntario. Ministry of Transportation. Safety Research Office, ed. Monocular vision and commercial motor vehicle safety. [Downsview, Ont.]: Safety Research Office, Safety Policy Branch, 1995.
Find full textBook chapters on the topic "Vision, Monocular"
Said, Engy T., and Bishoy Said. "Postoperative Monocular Vision Loss." In Clinical Anesthesiology, 453–61. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8696-1_54.
Full textPoeck, Klaus. "Monocular Loss of Vision." In Diagnostic Decisions in Neurology, 84–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-70693-6_22.
Full textMerriott, David, Steven Carter, and Lilangi S. Ediriwickrema. "Transient Monocular Vision Loss." In Controversies in Neuro-Ophthalmic Management, 171–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74103-7_17.
Full textLuo, Chong, and Wenjun Zeng. "Monocular and Binocular People Tracking." In Computer Vision, 1–4. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_872-1.
Full textShirai, Yoshiaki. "Shape from Monocular Images." In Three-Dimensional Computer Vision, 141–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-82429-6_8.
Full textVernon, David. "Monocular Vision — Segmentation in Additive Images." In Fourier Vision, 27–48. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1413-8_3.
Full textVernon, David. "Monocular Vision — Segmentation in Occluding Images." In Fourier Vision, 49–73. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1413-8_4.
Full textLi, Ruilong, Yuliang Xiu, Shunsuke Saito, Zeng Huang, Kyle Olszewski, and Hao Li. "Monocular Real-Time Volumetric Performance Capture." In Computer Vision – ECCV 2020, 49–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58592-1_4.
Full textLerasle, F., G. Rives, M. Dhome, and A. Yassine. "Human body tracking by monocular vision." In Lecture Notes in Computer Science, 518–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61123-1_166.
Full textSayd, Patrick, Michel Dhome, and Jean-Marc Lavest. "Recovering Generalized Cylinders by monocular vision." In Object Representation in Computer Vision II, 25–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61750-7_22.
Full textConference papers on the topic "Vision, Monocular"
Hwang, Jihye, Yeounggwang Ji, and Eun Yi Kim. "Monocular vision-based collision avoidance system." In the 14th international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2371664.2371688.
Full textYang, Jiaolong, Lei Chen, and Wei Liang. "Monocular vision based robot self-localization." In 2010 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2010. http://dx.doi.org/10.1109/robio.2010.5723497.
Full textRoyer, E., J. Bom, M. Dhome, B. Thuilot, M. Lhuillier, and F. Marmoiton. "Outdoor autonomous navigation using monocular vision." In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2005. http://dx.doi.org/10.1109/iros.2005.1545495.
Full textStein, Gregory J., Christopher Bradley, Victoria Preston, and Nicholas Roy. "Enabling Topological Planning with Monocular Vision." In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020. http://dx.doi.org/10.1109/icra40945.2020.9197484.
Full textChang, C. C., and X. H. Xiao. "Monocular vision technique for vibration measurement." In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2009. SPIE, 2009. http://dx.doi.org/10.1117/12.815424.
Full textSakamoto, Kunio, Kazuki Saruta, and Kazutoki Takeda. "Monocular multiview stereoscopic 3D vision system." In Photonics West 2001 - Electronic Imaging, edited by Stephen A. Benton, Sylvia H. Stevenson, and T. John Trout. SPIE, 2001. http://dx.doi.org/10.1117/12.429481.
Full textKumar, Anil, Hailin Ren, and Pinhas Ben-Tzvi. "Obstacle Identification for Vision Assisted Control Architecture of a Hybrid Mechanism Mobile Robot." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5324.
Full textEade, E. D., and T. W. Drummond. "Edge Landmarks in Monocular SLAM." In British Machine Vision Conference 2006. British Machine Vision Association, 2006. http://dx.doi.org/10.5244/c.20.2.
Full textCastelow, D. A., and A. J. Rerolle. "A Monocular Ground Plane Estimation System." In British Machine Vision Conference 1991. Springer-Verlag London Limited, 1991. http://dx.doi.org/10.5244/c.5.58.
Full textMarzorati, D., M. Matteucci, D. A. Migliore, and D. G. Sorrenti. "Monocular SLAM with Inverse Scaling Parametrization." In British Machine Vision Conference 2008. British Machine Vision Association, 2008. http://dx.doi.org/10.5244/c.22.94.
Full textReports on the topic "Vision, Monocular"
CuQlock-Knopp, V. G., Dawn E. Sipes, Warren Torgerson, Edward Bender, and John O. Merritt. Extended Use of Night Vision Goggles: An Evaluation of Comfort for Monocular and Biocular Configurations. Fort Belvoir, VA: Defense Technical Information Center, July 1997. http://dx.doi.org/10.21236/ada328485.
Full textCuQlock-Knopp, V. G., Warren Torgerson, Dawn E. Sipes, Edward Bender, and John O. Merritt. A Comparison of Monocular, Biocular, and Binocular Night Vision Goggles for Traversing Off-Road Terrain on Foot. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada294018.
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