Relevant bibliographies by topics / Visual recognition system

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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 'Visual recognition system'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Visual recognition system"

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Laszlo, Sarah, and Elizabeth Sacchi. "Individual differences in involvement of the visual object recognition system during visual word recognition." Brain and Language 145-146 (June 2015): 42–52. http://dx.doi.org/10.1016/j.bandl.2015.03.009.

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Gornostal, Alexandr, and Yaroslaw Dorogyy. "Development of audio-visual speech recognition system." ScienceRise 12, no. 1 (2017): 42–47. http://dx.doi.org/10.15587/2313-8416.2017.118212.

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Khosla, Deepak, David J. Huber, and Christopher Kanan. "A neuromorphic system for visual object recognition." Biologically Inspired Cognitive Architectures 8 (April 2014): 33–45. http://dx.doi.org/10.1016/j.bica.2014.02.001.

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ASAKURA, Toshiyuki, and Yasutomi IIDA. "Intelligent Visual Recognition System in Harvest Robot." Proceedings of the JSME annual meeting 2002.1 (2002): 195–96. http://dx.doi.org/10.1299/jsmemecjo.2002.1.0_195.

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Wong, Yee Wan, Kah Phooi Seng, and Li-Minn Ang. "Audio-Visual Recognition System in Compression Domain." IEEE Transactions on Circuits and Systems for Video Technology 21, no. 5 (2011): 637–46. http://dx.doi.org/10.1109/tcsvt.2011.2129670.

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CAO, JIANGTAO, NAOYUKI KUBOTA, PING LI, and HONGHAI LIU. "THE VISUAL-AUDIO INTEGRATED RECOGNITION METHOD FOR USER AUTHENTICATION SYSTEM OF PARTNER ROBOTS." International Journal of Humanoid Robotics 08, no. 04 (2011): 691–705. http://dx.doi.org/10.1142/s0219843611002678.

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Some of noncontact biometric ways have been used for user authentication system of partner robots, such as visual-based recognition methods and speech recognition. However, the methods of visual-based recognition are sensitive to the light noise and speech recognition systems are perturbed to the acoustic environment and sound noise. Inspiring from the human's capability of compensating visual information (looking) with audio information (hearing), a visual-audio integrating method is proposed to deal with the disturbance of light noise and to improve the recognition accuracy. Combining with t
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Malowany, Dan, and Hugo Guterman. "Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems." Algorithms 13, no. 7 (2020): 167. http://dx.doi.org/10.3390/a13070167.

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Computer vision is currently one of the most exciting and rapidly evolving fields of science, which affects numerous industries. Research and development breakthroughs, mainly in the field of convolutional neural networks (CNNs), opened the way to unprecedented sensitivity and precision in object detection and recognition tasks. Nevertheless, the findings in recent years on the sensitivity of neural networks to additive noise, light conditions, and to the wholeness of the training dataset, indicate that this technology still lacks the robustness needed for the autonomous robotic industry. In a
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Stork, David G. "Neural network acoustic and visual speech recognition system." Journal of the Acoustical Society of America 102, no. 3 (1997): 1282. http://dx.doi.org/10.1121/1.420021.

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Jiao, Chenlei, Binbin Lian, Zhe Wang, Yimin Song, and Tao Sun. "Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm." International Journal of Advanced Robotic Systems 17, no. 5 (2020): 172988142094872. http://dx.doi.org/10.1177/1729881420948727.

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Object recognition is a prerequisite to control a soft gripper successfully grasping an unknown object. Visual and tactile recognitions are two commonly used methods in a grasping system. Visual recognition is limited if the size and weight of the objects are involved, whereas the efficiency of tactile recognition is a problem. A visual–tactile recognition method is proposed to overcome the disadvantages of both methods in this article. The design and fabrication of the soft gripper considering the visual and tactile sensors are implemented, where the Kinect v2 is adopted for visual informatio
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Stringa, Luigi. "A VISUAL MODEL FOR PATTERN RECOGNITION." International Journal of Neural Systems 03, supp01 (1992): 31–39. http://dx.doi.org/10.1142/s0129065792000358.

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A general model for an optical recognition system capable of simultaneous recognition of patterns at different resolution levels is outlined. The model is based on two hierarchic stages of processing networks and presents interesting analogies with the human visual system. Illustrative applications and preliminary experimental results are also briefly discussed.
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Dissertations / Theses on the topic "Visual recognition system"

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Campbell, Larry W. "An intelligent tutor system for visual aircraft recognition." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/27723.

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Approved for public release; distribution is unlimited.<br>Visual aircraft recognition (VACR) is a critical skill for U.S. Army Short Range Air Defense (SHORAD) soldiers. It is the most reliable means of identifying aircraft, however VACR skills are not easy to teach or learn, and once learned they are highly degradable. The numerous training aids that exist to help units train soldiers require qualified instructors who are not always available. Also, the varying degrees of proficiency among soldiers make group training less than ideal. In an attempt to alleviate the problems in most VASC trai
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Dong, Junda. "Designing a Visual Front End in Audio-Visual Automatic Speech Recognition System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1382.

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Audio-visual automatic speech recognition (AVASR) is a speech recognition technique integrating audio and video signals as input. Traditional audio-only speech recognition system only uses acoustic information from an audio source. However the recognition performance degrades significantly in acoustically noisy environments. It has been shown that visual information also can be used to identify speech. To improve the speech recognition performance, audio-visual automatic speech recognition has been studied. In this paper, we focus on the design of the visual front end of an AVASR system, which
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Wojnowski, Christine. "Reasoning with visual knowledge in an object recognition system /." Online version of thesis, 1990. http://hdl.handle.net/1850/10596.

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Sun, Yongbin Ph D. Massachusetts Institute of Technology. "An RFID-based visual recognition system for the retail industry." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104277.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 63-67).<br>In this thesis, I aim to build an accurate fine-grained retail product recognition system for improving customer in-store shopping experience. To achieve high accuracy, I developed a two-phase visual recognition scheme to identify the viewed retail product by verifying different types of visual features. The proposed scheme is robust enough to distinguish visually similar products in the tests. However
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Koprnicky, Miroslav. "Towards a Versatile System for the Visual Recognition of Surface Defects." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/888.

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Automated visual inspection is an emerging multi-disciplinary field with many challenges; it combines different aspects of computer vision, pattern recognition, automation, and control systems. There does not exist a large body of work dedicated to the design of generalized visual inspection systems; that is, those that might easily be made applicable to different product types. This is an important oversight, in that many improvements in design and implementation times, as well as costs, might be realized with a system that could easily be made to function in different production enviro
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Sjöholm, Alexander. "Closing the Loop : Mobile Visual Location Recognition." Thesis, Linköpings universitet, Datorseende, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-112547.

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Visual simultaneous localization and mapping (SLAM) as field has been researched for ten years, but with recent advances in mobile performance visual SLAM is entering the consumer market in a completely new way. A visual SLAM system will however be sensitive to non cautious use that may result in severe motion, occlusion or poor surroundings in terms of visual features that will cause the system to temporarily fail. The procedure of recovering from such a fail is called relocalization. Together with two similar problems localization, to find your position in an existing SLAM session, and loop
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Su, Ying-fung. "Role of temporal texture in visual system exploration with computer simulations /." Click to view the E-thesis via HKUTO, 2010. http://sunzi.lib.hku.hk/hkuto/record/B43703768.

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Kaplan, Bernhard. "Modeling prediction and pattern recognition in the early visual and olfactory systems." Doctoral thesis, KTH, Beräkningsbiologi, CB, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166127.

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Our senses are our mind's window to the outside world and determine how we perceive our environment.Sensory systems are complex multi-level systems that have to solve a multitude of tasks that allow us to understand our surroundings.However, questions on various levels and scales remain to be answered ranging from low-level neural responses to behavioral functions on the highest level.Modeling can connect different scales and contribute towards tackling these questions by giving insights into perceptual processes and interactions between processing stages.In this thesis, numerical simulations
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Adjei-Kumi, Theophilus. "The development of an intelligent system for visual simulation of construction projects." Thesis, University of Strathclyde, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311845.

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Su, Ying-fung, and 蘇盈峰. "Role of temporal texture in visual system: exploration with computer simulations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B43703768.

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Books on the topic "Visual recognition system"

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Bastian, Leibe, ed. Visual object recognition. Morgan & Claypool, 2011.

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Information routing, correspondence finding, and object recognition in the brain. Springer-Verlag, 2010.

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Jinkins, J. Randy, and Claudia da Costa Leite. Neurodiagnostic imaging: Pattern analysis and differential diagnosis. Edited by Jinkins J. Randy and Leite Claudia da Costa. Lippincott-Raven, 1998.

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A, Tsihrintzis George, ed. Visual affect recognition. IOS Press, 2010.

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Liew, Alan Wee-Chung. Visual speech recognition: Lip segmentation and mapping. Edited by Wang Shilin. Medical Information Science Reference, 2009.

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Visual analysis of behaviour: From pixels to semantics. Springer-Verlag New York Inc, 2011.

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International Workshop on Visual Form (4th 2001 Capri, Italy). Visual form 2001: 4th International Workshop on Visual Form, IWVF4, Capri, Italy, May 28-30, 2001 : proceedings. Springer, 2001.

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Wayne, Cranton, Fihn Mark, and SpringerLink (Online service), eds. Handbook of Visual Display Technology. Springer Berlin Heidelberg, 2012.

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Werner, Backhaus, ed. Neuronal coding of perceptual systems: Proceedings of the International School of Biophysics, Casamicciola, Napoli, Italy, 12-17 October 1998. World Scientific, 2001.

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Salah, Albert Ali. Human Behavior Understanding: First International Workshop, HBU 2010, Istanbul, Turkey, August 22, 2010. Proceedings. Springer-Verlag Heidelberg, 2010.

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Book chapters on the topic "Visual recognition system"

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Cyganek, Bogusław, and Sławomir Gruszczyński. "Visual System for Drivers’ Eye Recognition." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21219-2_55.

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Neven, Hartmut. "Designing a Comprehensive Visual Recognition System." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11301-7_3.

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Batista, Jorge P. "A Real-Time Driver Visual Attention Monitoring System." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492429_25.

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Liu, Shaofeng, Yingchun Fan, Yuliang Tang, Xin Jing, Jintao Yao, and Hong Han. "Fuzzy Control Reversing System Based on Visual Information." In Pattern Recognition and Computer Vision. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31726-3_21.

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Cerman, Martin, Gayane Shalunts, and Daniel Albertini. "A Mobile Recognition System for Analog Energy Meter Scanning." In Advances in Visual Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50835-1_23.

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Neo, H. F., C. C. Teo, and Andrew B. J. Teoh. "A Wavelet-Based Face Recognition System Using Partial Information." In Advances in Visual Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17277-9_44.

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Peng, Han, and Abolfazl Razi. "Fully Autonomous UAV-Based Action Recognition System Using Aerial Imagery." In Advances in Visual Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64556-4_22.

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Kolawole, Akintola, and Alireza Tavakkoli. "A Novel Gait Recognition System Based on Hidden Markov Models." In Advances in Visual Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33191-6_13.

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Nseaf, Asama Kuder, Azizah Jaafar, Haroon Rashid, Riza Sulaiman, and Rahmita Wirza O. K. Rahmat. "Design Method of Video Based Iris Recognition System (V-IRS)." In Advances in Visual Informatics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02958-0_48.

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Dias, André, Jose Almeida, Alfredo Martins, and Eduardo Silva. "Real-Time Visual Ground-Truth System for Indoor Robotic Applications." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_36.

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Conference papers on the topic "Visual recognition system"

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Hsieh, Hsiang-Yu, Nanming Chen, and Ching-Lung Liao. "Visual Recognition System of Elastic Rail Clips for Mass Rapid Transit Systems." In ASME/IEEE 2007 Joint Rail Conference and Internal Combustion Engine Division Spring Technical Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/jrc/ice2007-40080.

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In recent years, the railway transportation system has become one of the main means of transportation. Therefore, driving safety is of great importance. However, because of the potential of multiple breaks of elastic rail clips in a fixed rail, accidents may occur when a train passes through the track. This paper presents the development of a computer visual recognition system which can detect the status of elastic rail clips. This visual recognition system can be used in mass rapid transit systems to reduce the substantial need of manpower for checking elastic rail clips at present. The visua
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Fukushima, Kazue, Harumi Kawamura, Makoto Kosugi, and Noburu Sonehara. "Personal system for practical face recognition." In Visual Communications and Image Processing '96, edited by Rashid Ansari and Mark J. T. Smith. SPIE, 1996. http://dx.doi.org/10.1117/12.233238.

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Tian, X., H. Deng, K. Yamazaki, M. Fujishima, and M. Mori. "On-Machine Visual Modeling System With Object Recognition." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81661.

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This paper presents an on-machine modeling system that tries to bridge the gap between the design and the machining. This system is able to build a comprehensive solid model of the CNC machining workspace after the workpiece and fixtures have been installed onto the working table. This solid model can be used for simulation to enhance its credibility. For this purpose, one prototype of a 3D visual modeling system is proposed and designed. In order to accurately calibrate CCD cameras upon the absolute coordinate frame of the machining center, a practical calibration method is presented at first
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Frisky, Aufaclav Zatu Kusuma, Chien-Yao Wang, Andri Santoso, and Jia-Ching Wang. "Lip-based visual speech recognition system." In 2015 International Carnahan Conference on Security Technology (ICCST). IEEE, 2015. http://dx.doi.org/10.1109/ccst.2015.7389703.

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Wu, Shen, Feng Jiang, Debin Zhao, Shaohui Liu, and Wen Gao. "Viewpoint-independent hand gesture recognition system." In 2012 Visual Communications and Image Processing (VCIP). IEEE, 2012. http://dx.doi.org/10.1109/vcip.2012.6410809.

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Nguyen, Mao, and Minh-Triet Tran. "Toward a practical visual object recognition system." In the Fourth Symposium. ACM Press, 2013. http://dx.doi.org/10.1145/2542050.2542077.

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Kim, Min-Uk, and Kyoungro Yoon. "Image Recognition System That Uses Visual Word." In 2014 International Conference on Information Science and Applications (ICISA). IEEE, 2014. http://dx.doi.org/10.1109/icisa.2014.6847410.

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Shdaifat, I., and R. R. Grigat. "A system for audio-visual speech recognition." In Interspeech 2005. ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-367.

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Kobayashi, Seiji. "Optical gesture recognition system." In ACM SIGGRAPH 97 Visual Proceedings: The art and interdisciplinary programs of SIGGRAPH '97. ACM Press, 1997. http://dx.doi.org/10.1145/259081.259206.

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Wu, Yuefeng, Nong Sang, Wei Lin, and Yuanjie Shao. "Joint image restoration and location in visual navigation system." In Automatic Target Recognition and Navigation, edited by Jayaram K. Udupa, Hanyu Hong, and Jianguo Liu. SPIE, 2018. http://dx.doi.org/10.1117/12.2284978.

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Reports on the topic "Visual recognition system"

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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-c
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Bajcsy, Ruzena. A Query Driven Computer Vision System: A Paradigm for Hierarchical Control Strategies during the Recognition Process of Three-Dimensional Visually Perceived Objects. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada185507.

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