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Готові списки джерел за темами / Computer vision

Добірка наукової літератури з теми "Computer vision"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 22 червня 2024

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Статті в журналах з теми "Computer vision":

1

Deshmukh, Omkar Madhukar. "Computer Vision." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1237–39. http://dx.doi.org/10.22214/ijraset.2021.35926.

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Computer vision may be a field of computer science that trains computers to interpret and perceive the visual world. exploitation digital pictures from cameras and videos and deep learning models, machines will accurately determine and classify objects — and so react to what they "see.”. Computer vision is Associate in Nursing knowledge domain scientific field that deals with however computers will gain high-level understanding from digital pictures or videos. From the angle of engineering, it seeks to grasp and alter tasks that the human sensory system will do. Computer vision tasks embrace strategies for exploit, processing, analyzing and understanding digital pictures, and extraction of high-dimensional knowledge from the important world so as to supply numerical or symbolic info, e.g. within the styles of selections. Understanding during this context suggests that the transformation of visual pictures (the input of the retina) into descriptions of the planet that be to thought processes and might elicit acceptable action. This image understanding will be seen because the disentangling of symbolic info from image knowledge mistreatment models created with the help of pure mathematics, physics, statistics, and learning theory.

2

Pandey, Mrs Arjoo. "Computer Vision." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (July 31, 2023): 510–14. http://dx.doi.org/10.22214/ijraset.2023.54701.

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Abstract: Computer vision is a field of artificial intelligence that focuses on enabling computers to understand and interpret visual information from images or videos. It involves developing algorithms and techniques to extract meaningful insights, patterns, and knowledge from visual data, mimicking human visual perception capabilities. The abstract of computer vision encompasses a range of fundamental tasks and objectives, including: Image Classification: Classifying images into predefined categories or classes, such as distinguishing between different objects, animals, or scenes. Object Detection and Recognition: Locating and identifying specific objects within an image or video, often through the use of bounding boxes or pixel-level segmentation. Semantic Segmentation: Assigning semantic labels to each pixel in an image to distinguish between different objects or regions.

3

Randolph, Susan A. "Computer Vision Syndrome." Workplace Health & Safety 65, no. 7 (June 19, 2017): 328. http://dx.doi.org/10.1177/2165079917712727.

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With the increased use of electronic devices with visual displays, computer vision syndrome is becoming a major public health issue. Improving the visual status of workers using computers results in greater productivity in the workplace and improved visual comfort.

4

S, Anusha, Nayana Shree A, Nithin R, Pavan Prabhu N, and Rahul D M. "Computer Vision Based Workout Application." International Journal of Research Publication and Reviews 4, no. 4 (April 23, 2023): 4088–91. http://dx.doi.org/10.55248/gengpi.4.423.37565.

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5

MATSUYAMA, TAKASHI. "Computer Vision." Journal of the Institute of Electrical Engineers of Japan 116, no. 5 (1996): 263–66. http://dx.doi.org/10.1541/ieejjournal.116.263.

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6

Aloimonos, Y., and A. Rosenfeld. "Computer vision." Science 253, no. 5025 (September 13, 1991): 1249–54. http://dx.doi.org/10.1126/science.1891713.

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7

Marchant, J. A., and F. E. Sistler. "Computer vision." Computers and Electronics in Agriculture 9, no. 1 (August 1993): vii—viii. http://dx.doi.org/10.1016/0168-1699(93)90024-u.

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8

Mamrega, V. V. "COMPUTER VISION." SYNCHROINFO JOURNAL 8, no. 5 (2022): 7–11. http://dx.doi.org/10.36724/2664-066x-2022-8-5-7-11.

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This article explores the subject of computer vision systems – a technology that allows vehicles to identify, track, and also classify objects on the roadway. The objectives of the study are to consider the principle of operation of these automated systems, their advantages in comparison with modern road regulation, as well as the problems of implementation and development of these systems. The research was carried out on the basis of the analysis of information from open information resources. The statistics of accidents at work are presented, the high rates of which are due to large volumes of production and an outdated system for monitoring compliance with safety rules and the availability of personal protective equipment for employees. The scheme of interaction of the components of a computer vision system is considered, which will allow monitoring of events occurring in production during operation, monitoring the situation at the enterprise for the occurrence of a potentially dangerous situation for personnel and equipment, and, accordingly, this system will be able to prevent an emergency, as well as avoid personal injury by reacting even to minor deviations from operating parameters. The research was carried out on the basis of the study and analysis of materials published in open information sources.

9

Kaushik, C. S. S. Krishna, Prathit Panda, P. S. S. Asrith, M. Patrick Rozario, and Prof Ayain John. "Computer Vision Integrated Website." International Journal of Innovative Technology and Exploring Engineering 13, no. 2 (January 30, 2024): 20–25. http://dx.doi.org/10.35940/ijitee.b9783.13020124.

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Computer vision is an integral part of artificial intelligence that empowers machines to perceive the world similar to human vision. Despite its extensive evolution, widespread awareness of its potential remains limited. The goal of the "Computer Vision Integrated Website" paper is to enhance awareness and exhibit the capabilities of computer vision. By creating an accessible platform featuring various computer vision models, authors aim to captivate audiences and drive growth in the field. The paper seeks to illustrate how computers interpret visual information by integrating user-friendly computer vision models into a website. Through practical demonstrations like emotion detection and pose estimation, authors intend to showcase the potential of computer vision in everyday scenarios. Ultimately, authors strive to narrow the knowledge gap between technical advancements in computer vision and public understanding, fostering curiosity and encouraging broader interest in the technology.

10

Bouma, Herman. "Human Vision and Computer Vision." Contemporary Psychology: A Journal of Reviews 30, no. 1 (January 1985): 47. http://dx.doi.org/10.1037/023481.

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Дисертації з теми "Computer vision":

1

Revell, James Duncan. "Computer vision elastography." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412361.

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2

Chiu, Kevin (Kevin Geeyoung). "Vision on tap : an online computer vision toolkit." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/67714.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 60-64).
In this thesis, we present an online toolkit, based on a combination of a Scratch-based programming environment and computer vision libraries, manifested as blocks within the environment, integrated with a community platform for diffusing advances in computer vision to a general populace. We show that by providing these tools, non-developers are able to create and publish computer vision applications. The visual development environment includes a collection of algorithms that, despite being well known in the computer vision community, provide capabilities to commodity cameras that are not yet common knowledge. In support of this visual development environment, we also present an online community that allows users to share applications made in the environment, assisting the dissemination of both the knowledge of camera capabilities and advanced camera capabilities to users who have not yet been exposed to their existence or comfortable with their use. Initial evaluations consist of user studies that quantify the abilities afforded to the novice computer vision users by the toolkit, baselined against experienced computer vision users.
by Kevin Chiu.
S.M.

3

Rihan, Jonathan. "Computer vision based interfaces for computer games." Thesis, Oxford Brookes University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.579554.

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Interacting with a computer game using only a simple web camera has seen a great deal of success in the computer games industry, as demonstrated by the numerous computer vision based games available for the Sony PlayStation 2 and PlayStation 3 game consoles. Computational efficiency is important for these human computer inter- action applications, so for simple interactions a fast background subtraction approach is used that incorporates a new local descriptor which uses a novel temporal coding scheme that is much more robust to noise than the standard formulations. Results are presented that demonstrate the effect of using this method for code label stability. Detecting local image changes is sufficient for basic interactions, but exploiting high-level information about the player's actions, such as detecting the location of the player's head, the player's body, or ideally the player's pose, could be used as a cue to provide more complex interactions. Following an object detection approach to this problem, a combined detection and segmentation approach is explored that uses a face detection algorithm to initialise simple shape priors to demonstrate that good real-time performance can be achieved for face texture segmentation. Ultimately, knowing the player's pose solves many of the problems encountered by simple local image feature based methods, but is a difficult and non-trivial problem. A detection approach is also taken to pose estimation: first as a binary class problem for human detection, and then as a multi-class problem for combined localisation and pose detection. For human detection, a novel formulation of the standard chamfer matching algo- rithm as an SVM classifier is proposed that allows shape template weights to be learnt automatically. This allows templates to be learnt directly from training data even in the presence of background and without the need to pre-process the images to extract their silhouettes. Good results are achieved when compared to a state of the art human detection classifier. For combined pose detection and localisation, a novel and scalable method of ex- ploiting the edge distribution in aligned training images is presented to select the most potentially discriminative locations for local descriptors that allows a much higher space of descriptor configurations to be utilised efficiently. Results are presented that show competitive performance when compared to other combined localisation and pose detection methods.

4

Klomark, Marcus. "Occupant Detection using Computer Vision." Thesis, Linköping University, Linköping University, Computer Vision, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54363.

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The purpose of this master’s thesis was to study the possibility to use computer vision methods to detect and classify objects in the front passenger seat in a car. This work presents different approaches to solve this problem and evaluates the usefulness of each technique. The classification information should later be used to modulate the speed and the force of the airbag, to be able to provide each occupant with optimal protection and safety.

This work shows that computer vision has a great potential in order to provide data, which may be used to perform reliable occupant classification. Future choice of method to use depends on many factors, for example costs and requirements on the system from laws and car manufacturers. Further, evaluation and tests of the methods in this thesis, other methods, the ABE approach and post-processing of the results should also be made before a reliable classification algorithm may be written.

5

Purdy, Eric. "Grammatical methods in computer vision." Thesis, The University of Chicago, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3557428.

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In computer vision, grammatical models are models that represent objects hierarchically as compositions of sub-objects. This allows us to specify rich object models in a standard Bayesian probabilistic framework. In this thesis, we formulate shape grammars, a probabilistic model of curve formation that allows for both continuous variation and structural variation. We derive an EM-based training algorithm for shape grammars. We demonstrate the effectiveness of shape grammars for modeling human silhouettes, and also demonstrate their effectiveness in classifying curves by shape. We also give a general method for heuristically speeding up a large class of dynamic programming algorithms. We provide a general framework for discussing coarse-to-fine search strategies, and provide proofs of correctness. Our method can also be used with inadmissible heuristics.

Finally, we give an algorithm for doing approximate context-free parsing of long strings in linear time. We define a notion of approximate parsing in terms of restricted families of decompositions, and construct small families which can approximate arbitrary parses.

6

Newman, Rhys A. "Automatic learning in computer vision." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390526.

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7

Crossley, Simon. "Robust temporal stereo computer vision." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327614.

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8

Fletcher, Gordon James. "Geometrical problems in computer vision." Thesis, University of Liverpool, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337166.

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9

Mirmehdi, Majid. "Transputer configurations for computer vision." Thesis, City University London, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292339.

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10

Hovhannisyan, Vahan. "Multilevel optimisation for computer vision." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/55874.

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The recent spark in machine learning and computer vision methods requiring increasingly larger datasets has motivated the introduction of optimisation algorithms specifically tailored to solve very large problems within practical time constraints. This demand in algorithms challenges the practicability of state of the art methods requiring new approaches that can take advantage of not only the problem’s mathematical structure, but also its data structure. Fortunately, such structure is present in many computer vision applications, where the problems can be modelled with varying degrees of fidelity. This structure suggests using multiscale models and thus multilevel algorithms. The objective of this thesis is to develop, implement and test provably convergent multilevel optimisation algorithms for convex composite optimisation problems in general and its applications in computer vision in particular. Our first multilevel algorithm solves convex composite optimisation problem and it is most efficient particularly for the robust facial recognition task. The method uses concepts from proximal gradient, mirror descent and multilevel optimisation algorithms, thus we call it multilevel accelerated gradient mirror descent algorithm (MAGMA). We first show that MAGMA has the same theoretical convergence rate as the state of the art first order methods and has much lower per iteration complexity. Then we demonstrate its practical advantage on many facial recognition problems. The second part of the thesis introduces new multilevel procedure most appropriate for the robust PCA problems requiring iterative SVD computations. We propose to exploit the multiscale structure present in these problems by constructing lower dimensional matrices and use its singular values for each iteration of the optimisation procedure. We implement this approach on three different optimisation algorithms - inexact ALM, Frank-Wolfe Thresholding and non-convex alternating projections. In this case as well we show that these multilevel algorithms converge (to an exact or approximate) solution with the same convergence rate as their standard counterparts and test all three methods on numerous synthetic and real life problems demonstrating that the multilevel algorithms are not only much faster, but also solve problems that often cannot be solved by their standard counterparts.

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Книги з теми "Computer vision":

1

Szeliski, Richard. Computer Vision. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-34372-9.

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2

Zha, Honbin, Xilin Chen, Liang Wang, and Qiguang Miao, eds. Computer Vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48558-3.

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3

Zha, Hongbin, Xilin Chen, Liang Wang, and Qiguang Miao, eds. Computer Vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48570-5.

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4

Cipolla, Roberto, Sebastiano Battiato, and Giovanni Maria Farinella, eds. Computer Vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12848-6.

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5

Yang, Jinfeng, Qinghua Hu, Ming-Ming Cheng, Liang Wang, Qingshan Liu, Xiang Bai, and Deyu Meng, eds. Computer Vision. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7299-4.

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6

Yang, Jinfeng, Qinghua Hu, Ming-Ming Cheng, Liang Wang, Qingshan Liu, Xiang Bai, and Deyu Meng, eds. Computer Vision. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7302-1.

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Yang, Jinfeng, Qinghua Hu, Ming-Ming Cheng, Liang Wang, Qingshan Liu, Xiang Bai, and Deyu Meng, eds. Computer Vision. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7305-2.

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8

Klette, Reinhard, Andreas Koschan, and Karsten Schlüns. Computer Vision. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-663-11999-9.

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9

Szeliski, Richard. Computer Vision. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84882-935-0.

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10

Priese, Lutz. Computer Vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45129-8.

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Частини книг з теми "Computer vision":

1

Breiding, Paul, Kathlén Kohn, and Bernd Sturmfels. "Computer Vision." In Oberwolfach Seminars, 163–74. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51462-3_13.

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Анотація:

AbstractThe field of computer vision studies how computers can gain understanding from images and videos, similar to human cognitive abilities. One of the classical challenges is to reconstruct a 3D object from images taken by several unknown cameras. While the resulting questions in multiview geometry [80] have a long history in computer vision, recent years have seen a confluence with ideas and algorithms from algebraic geometry.

2

Huang, T. S. "Computer Vision." In Advances in Computer Graphics, 141–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76286-4_4.

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3

Bhattacharjee, Joydeep. "Computer Vision." In Practical Machine Learning with Rust, 229–76. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5121-8_6.

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4

Lee, Raymond S. T. "Computer Vision." In Artificial Intelligence in Daily Life, 119–55. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7695-9_5.

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5

Nath, Vishnu, and Stephen E. Levinson. "Computer Vision." In Autonomous Robotics and Deep Learning, 31–38. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05603-6_5.

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6

Nath, Vishnu, and Stephen E. Levinson. "Computer Vision." In SpringerBriefs in Computer Science, 33–38. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05606-7_5.

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7

Du, Cheng-Jin, and Qiaofen Cheng. "Computer Vision." In Food Engineering Series, 157–81. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0311-5_7.

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8

Poggio, Tomaso. "Computer Vision." In Biological and Artificial Intelligence Systems, 471–83. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-3117-6_21.

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Ng, Tian Seng. "Computer Vision." In Real Time Control Engineering, 153–65. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1509-0_10.

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Jin, Miao, Xianfeng Gu, Ying He, and Yalin Wang. "Computer Vision." In Conformal Geometry, 103–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75332-4_7.

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Тези доповідей конференцій з теми "Computer vision":

1

Wang, Yuanbo, Osama Sakhi, Ala Eddine Ayadi, Matthew Hagen, and Estelle Afshar. "Computer Vision." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3406710.

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2

Poggio, Tomaso. "Computer Vision." In OE LASE'87 and EO Imaging Symp (January 1987, Los Angeles), edited by Francis J. Corbett. SPIE, 1987. http://dx.doi.org/10.1117/12.940005.

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"Computer vision." In 2016 9th International Conference on Human System Interactions (HSI). IEEE, 2016. http://dx.doi.org/10.1109/hsi.2016.7529647.

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"Computer vision." In 2017 10th International Conference on Human System Interactions (HSI). IEEE, 2017. http://dx.doi.org/10.1109/hsi.2017.8005033.

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"Computer vision." In 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2017. http://dx.doi.org/10.1109/iccp.2017.8117013.

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"Computer vision." In 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2016. http://dx.doi.org/10.1109/iccp.2016.7737143.

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"Computer vision." In 2011 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2011. http://dx.doi.org/10.1109/iccp.2011.6047868.

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"Computer vision." In 2010 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2010. http://dx.doi.org/10.1109/iccp.2010.5606451.

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"Computer vision." In 2012 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2012. http://dx.doi.org/10.1109/iccp.2012.6356171.

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"Computer vision." In 2013 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2013. http://dx.doi.org/10.1109/iccp.2013.6646083.

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Звіти організацій з теми "Computer vision":

1

Michalski, R. S., A. Rosenfeld, Y. Aloimonos, Z. Duric, and M. Maloof. Computer Vision through Learning. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada341212.

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2

Jones, J. (Computer vision and robotics). Office of Scientific and Technical Information (OSTI), February 1989. http://dx.doi.org/10.2172/6860370.

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Poggio, Tomaso, and James Little. Parallel Algorithms for Computer Vision. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada203947.

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4

Rosenfeld, Azriel. Qualitative Methods in Computer Vision. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada264335.

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5

Waggett, Michael L. Night Vision Goggles Computer Based Training. Fort Belvoir, VA: Defense Technical Information Center, April 1999. http://dx.doi.org/10.21236/ada398875.

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6

Fujiwara, Yoshimasa, Shree Nayer, and Katsushi Ikeuchi. Appearance Simulator for Computer Vision Research. Fort Belvoir, VA: Defense Technical Information Center, August 1991. http://dx.doi.org/10.21236/ada240506.

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7

Berry, Nina M., and Teresa H. Ko. On computer vision in wireless sensor networks. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/919195.

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8

Rodriguez, Simon, Autumn Toney, and Melissa Flagg. Patent Landscape for Computer Vision: United States and China. Center for Security and Emerging Technology, September 2020. http://dx.doi.org/10.51593/20200054.

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Анотація:

China’s surge in artificial intelligence development has been fueled, in large part, by advances in computer vision, the AI subdomain that makes powerful facial recognition technologies possible. This data brief compares U.S. and Chinese computer vision patent data to illustrate the different approaches each country takes to AI development.

9

Campbell, Nancy, Glenn Osga, David Kellmeyer, Daniel Lulue, and Earl Williams. A Human-Computer Interface Vision for Naval Transformation. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada427415.

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10

Wright, John, Yi Ma, Julien Mairal, Guillermo Sapiro, Thomas Huang, and Shuicheng Yan. Sparse Representation for Computer Vision and Pattern Recognition. Fort Belvoir, VA: Defense Technical Information Center, May 2009. http://dx.doi.org/10.21236/ada513248.

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