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Thematische Bibliographien / Computer vision / Zeitschriftenartikel

Zeitschriftenartikel zum Thema „Computer vision“

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Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 22. Juni 2024

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1

Deshmukh, Omkar Madhukar. „Computer Vision“. International Journal for Research in Applied Science and Engineering Technology 9, Nr. VII (15.07.2021): 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, Nr. 7 (31.07.2023): 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, Nr. 7 (19.06.2017): 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.

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S, Anusha, Nayana Shree A, Nithin R, Pavan Prabhu N und Rahul D M. „Computer Vision Based Workout Application“. International Journal of Research Publication and Reviews 4, Nr. 4 (23.04.2023): 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, Nr. 5 (1996): 263–66. http://dx.doi.org/10.1541/ieejjournal.116.263.

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6

Aloimonos, Y., und A. Rosenfeld. „Computer vision“. Science 253, Nr. 5025 (13.09.1991): 1249–54. http://dx.doi.org/10.1126/science.1891713.

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7

Marchant, J. A., und F. E. Sistler. „Computer vision“. Computers and Electronics in Agriculture 9, Nr. 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, Nr. 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 und Prof Ayain John. „Computer Vision Integrated Website“. International Journal of Innovative Technology and Exploring Engineering 13, Nr. 2 (30.01.2024): 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, Nr. 1 (Januar 1985): 47. http://dx.doi.org/10.1037/023481.

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Dhamodaran, Sasikala, Pratyush Pranjal Phukan, Mayank Singh und Shijin Nandakumar. „Review on Computer Vision Using OpenCV“. International Journal of Research Publication and Reviews 5, Nr. 5 (17.05.2024): 8608–21. http://dx.doi.org/10.55248/gengpi.5.0524.1345.

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12

Nemov, A. S., und N. Yu Vyatkina. „Computer vision syndrome“. Glavvrač (Chief Medical Officer), Nr. 6 (27.06.2023): 56–60. http://dx.doi.org/10.33920/med-03-2306-05.

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Technological advances in computing and Internet access enable workers to process more information and be more productive. It also means that workers are spending more time on electronic devices with visual displays such as computers, laptops, smartphones, tablets, e-readers, and even watches that contribute to eye strain. The same applies to children, as they spend many hours each day using electronic devices with digital displays to complete school assignments, play video games, and communicate in the Internet [4]. With the increasing use of these electronic devices, computer vision syndrome is becoming a major public health problem.

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Freeman, William T., Paul A. Beardsley, Hiroshi Kage, Ken-Ichi Tanaka, Kazuo Kyuma und Craig D. Weissman. „Computer vision for computer interaction“. ACM SIGGRAPH Computer Graphics 33, Nr. 4 (04.11.1999): 65–68. http://dx.doi.org/10.1145/345370.345417.

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14

Hayes, Brian. „Computer Vision and Computer Hallucinations“. American Scientist 103, Nr. 6 (2015): 380. http://dx.doi.org/10.1511/2015.117.380.

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Matsuzaka, Yasunari, und Ryu Yashiro. „AI-Based Computer Vision Techniques and Expert Systems“. AI 4, Nr. 1 (23.02.2023): 289–302. http://dx.doi.org/10.3390/ai4010013.

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Computer vision is a branch of computer science that studies how computers can ‘see’. It is a field that provides significant value for advancements in academia and artificial intelligence by processing images captured with a camera. In other words, the purpose of computer vision is to impart computers with the functions of human eyes and realise ‘vision’ among computers. Deep learning is a method of realising computer vision using image recognition and object detection technologies. Since its emergence, computer vision has evolved rapidly with the development of deep learning and has significantly improved image recognition accuracy. Moreover, an expert system can imitate and reproduce the flow of reasoning and decision making executed in human experts’ brains to derive optimal solutions. Machine learning, including deep learning, has made it possible to ‘acquire the tacit knowledge of experts’, which was not previously achievable with conventional expert systems. Machine learning ‘systematises tacit knowledge’ based on big data and measures phenomena from multiple angles and in large quantities. In this review, we discuss some knowledge-based computer vision techniques that employ deep learning.

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Amalia, Husnun. „Computer vision syndrome“. Jurnal Biomedika dan Kesehatan 1, Nr. 2 (27.09.2018): 117–18. http://dx.doi.org/10.18051/jbiomedkes.2018.v1.117-118.

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Computer Vision Syndrome (CVS) adalah keluhan gangguan penglihatan yang disebabkan oleh penggunaan komputer. Keluhan ini berhubungan dengan penggunaan Visual Display Terminal (VDT). Pada kehidupan modern, VDT adalah alat yang telah menjadi sebuah kebutuhan dalam kehidupan sehari-hari dan alat-alat ini harus selalu tersedia sebagai sarana di intitusi pendidikan, perkantoran dan di rumah. Alat yang termasuk VDT adalah monitor komputer, telepon genggam, tablet, laptop, handheld konsol dan lain-lain. Saat ini komputer sangat membantu aktivitas manusia namun monitor komputer mengeuarkan radiasi dan gelombang seperti sinar ultraviolet dan sinar X yang bila terpapar dalam jangka waktu lama akan mengakibatkan gangguan fisiologis pada mata. Pevalensi CVS mencapai 64-90% pada pengguna VDT dengan jumlah penderita di seluruh dunia diperkirakan sebesar 60 juta orang dan setiap tahun akan terus muncul 1 juta kasus baru. Prevalensi CVS pada mahasiswa teknik mencapai 81,9% lebih tinggi dibandingkan mahasiswa kedokteran yaitu sebesar 78,6%. Computer Vision Syndrome dapat terjadi pada anak-anak dan keluhan CVS pada ada anak-anak akan muncul lebih cepat dibandingkan pada orang dewasa. Komputer didisain untuk digunakan oleh orang dewasakomputer tidak ergonomik untuk digunakan oleh anak-anak. Hal ini menyebabkan CVS pada anak-anak akan disertai dengan keluhan muskuloskeletal.

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Arif, Khan Mohammad, und Md Jahangir Alam. „Computer Vision Syndrome“. Faridpur Medical College Journal 10, Nr. 1 (30.05.2016): 33–35. http://dx.doi.org/10.3329/fmcj.v10i1.27923.

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Computer Vision Syndrome (CVS) is a condition in which a person experience one or more of eye symptoms and/or headache and back pain as a result of prolonged working on a computer. Bankers, account section workers, professional computer workers, excessive near work by mobile, laptop or tab users are commonly affected. Headache, eye strain, dryness, burning, grittiness, heaviness or watering, stiff shoulders, low back pain and general fatigue are main symptoms. The duration of computer work is directly related to eye symptoms; and longer duration tends to result in long-lasting complaints even after the work is finished. Professional workers should practice preventive measure. Correction of refractive errors, modification of work station, using antiglare screen filter and hourly eye exercise are the chief preventive strategy. Neglected person may have less/decreased working capacity and reduced productivity.Faridpur Med. Coll. J. Jan 2015;10(1): 33-35

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Fedorov, Yu V., und A. Yu Fedorova. „Computer vision therapy“. Izvestiâ vysših učebnyh zavedenij. Priborostroenie 61, Nr. 3 (27.03.2018): 281–85. http://dx.doi.org/10.17586/0021-3454-2018-61-3-281-285.

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Hassner, Tal, und Itzik Bayaz. „Teaching Computer Vision“. ACM Transactions on Computing Education 14, Nr. 4 (24.02.2015): 1–17. http://dx.doi.org/10.1145/2597627.

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Brilakis, Ioannis, und Carl Haas. „Infrastructure computer vision“. Advanced Engineering Informatics 29, Nr. 2 (April 2015): 147–48. http://dx.doi.org/10.1016/j.aei.2015.04.003.

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Schiele, Bernt, und Gerhard Sagerer. „Computer vision systems“. Machine Vision and Applications 14, Nr. 1 (01.04.2003): 3–4. http://dx.doi.org/10.1007/s00138-002-0113-y.

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Wimalasundera, Saman. „Computer vision syndrome“. Galle Medical Journal 11, Nr. 1 (28.09.2009): 25. http://dx.doi.org/10.4038/gmj.v11i1.1115.

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Grimson, W. E. L., und J. L. Mundy. „Computer vision applications“. Communications of the ACM 37, Nr. 3 (März 1994): 44–51. http://dx.doi.org/10.1145/175247.175251.

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Syndrome, Computer Vision. „Computer Vision Syndrome“. Nursing Journal of India C, Nr. 10 (2009): 236–37. http://dx.doi.org/10.48029/nji.2009.c1003.

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25

Jolion, J. M. „Computer Vision Methodologies“. CVGIP: Image Understanding 59, Nr. 1 (Januar 1994): 53–71. http://dx.doi.org/10.1006/ciun.1994.1004.

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Jolion, J. „Computer Vision Methodologies“. Computer Vision and Image Understanding 59, Nr. 1 (Januar 1994): 53–71. http://dx.doi.org/10.1006/cviu.1994.1004.

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Nuzuliawati, Utin Alvina, Joan Sherlone Hutabarat und Antonia Kartika Indriati. „Computer Vision Syndrome“. Oftalmologi: Jurnal Kesehatan Mata Indonesia 4, Nr. 3 (30.12.2022): 66–70. http://dx.doi.org/10.11594/ojkmi.v4i3.40.

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Kemajuan di bidang teknologi saat ini menyebabkan tingginya pengguna perangkat elektronik pada semua kelompok umur. Terdapat perbedaan kebutuhan visual ketika seseorang melihat tampilan dilayar perangkat elektronik dibandingkan dengan materi cetak. Mata harus bekerja lebih keras saat melihat tampilan di layar perangkat elektronik, sehingga penggunaan perangkat elektronik dalam waktu lama dapat menyebabkan sekelompok keluhan pada mata yaitu kelelahan mata, iritasi, kemerahan, kekeringan, penglihatan kabur dan ganda yang disebut juga dengan computer vision syndrome. Menghilangkan faktor penyebab merupakan pengelolaan paling penting dalam mengatasi computer vision syndrome. Tinjauan pustaka ini bertujuan untuk menganalisis informasi terkini tentang prevalensi, gejala, patogenesis dan penatalaksanaan computer vision syndrome. Kesimpulan dari studi ini yaitu computer vision syndrome merupakan salah satu masalah kesehatan masyarakat utama yang berdampak besar pada penurunan kualitas hidup dan efisiensi di tempat kerja yang dapat dicegah dengan pemeriksaan mata rutin, menjaga kesehatan mata, posisi ergonomis dan lingkungan yang mendukung.

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Cazorla, Miguel, und Diego Viejo. „JavaVis: An integrated computer vision library for teaching computer vision“. Computer Applications in Engineering Education 23, Nr. 2 (24.12.2013): 258–67. http://dx.doi.org/10.1002/cae.21594.

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Che, Chang, Haotian Zheng, Zengyi Huang, Wei Jiang und Bo Liu. „Intelligent robotic control system based on computer vision technology“. Applied and Computational Engineering 64, Nr. 1 (15.05.2024): 150–55. http://dx.doi.org/10.54254/2755-2721/64/20241373.

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Computer vision is a kind of simulation of biological vision using computers and related equipment. It is an important part of the field of artificial intelligence. Its research goal is to make computers have the ability to recognize three-dimensional environmental information through two-dimensional images. Computer vision is based on image processing technology, signal processing technology, probability statistical analysis, computational geometry, neural network, machine learning theory and computer information processing technology, through computer analysis and processing of visual information.The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency, quality, and environmental sustainability. The article also discusses methodologies for developing intelligent garbage sorting robots, emphasizing the application of computer vision image recognition, feature extraction, and reinforcement learning techniques. Overall, the integration of computer vision technology with robot control holds promise for enhancing human-computer interaction, intelligent manufacturing, and environmental protection efforts.

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Bunin, Y. V., E. V. Vakulik, R. N. Mikhaylusov, V. V. Negoduyko, K. S. Smelyakov und O. V. Yasinsky. „Estimation of lung standing size with the application of computer vision algorithms“. Experimental and Clinical Medicine 89, Nr. 4 (17.12.2020): 87–94. http://dx.doi.org/10.35339/ekm.2020.89.04.13.

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Evaluation of spiral computed tomography data is important to improve the diagnosis of gunshot wounds and the development of further surgical tactics. The aim of the work is to improve the results of the diagnosis of foreign bodies in the lungs by using computer vision algorithms. Image gradation correction, interval segmentation, threshold segmentation, three-dimensional wave method, principal components method are used as a computer vision device. The use of computer vision algorithm allows to clearly determine the size of the foreign body of the lung with an error of 6.8 to 7.2%, which is important for in-depth diagnosis and development of further surgical tactics. Computed vision techniques increase the detail of foreign bodies in the lungs and have significant prospects for the use of spiral computed tomography for in-depth data processing. Keywords: computer vision, spiral computed tomography, lungs, foreign bodies.

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Ohta, Yuichi. „3D Image Media and Computer Vision -From CV as Robot Technology to CV as Media Technology-“. Journal of Robotics and Mechatronics 9, Nr. 2 (20.04.1997): 92–97. http://dx.doi.org/10.20965/jrm.1997.p0092.

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The possibility to apply the computer vision technology to the development of a new image medium is discussed. Computer vision has been studied as a sensor technology between the real world and computers. On the other hand, the computer graphics are the interface technology between the computers and human beings. The invention of ""3D photography"" based on the computer vision technology will realize a new 3D image medium which connects the real world and the human beings via computer. In such a framework, computer vision should be studied as a media technology rather than a robot technology.

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Syarifah Rohaya und Hafizh Shidqi. „Pencegahan Computer Vision Syndrome“. Jurnal Ilmiah Kedokteran dan Kesehatan 2, Nr. 3 (02.08.2023): 148–53. http://dx.doi.org/10.55606/klinik.v2i3.1919.

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Currently in the modern era, the development of science and technology is very rapid. More and more new technologies are emerging. Electronics make up a large part of everyday life at home, at work, and during leisure time. The use of desktops, laptops, computers, tablets, smart phones and reading from electronic devices has been used by everyone. While most of these uses of electronics can make life easier, there can also be negative effects. Long-term use can have an adverse effect on eye health known as computer vision syndrome.

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Rakhimov, Bakhtiyar Saidovich, Feroza Bakhtiyarovna Rakhimova, Sabokhat Kabulovna Sobirova, Furkat Odilbekovich Kuryazov und Dilnoza Boltabaevna Abdirimova. „Review And Analysis Of Computer Vision Algorithms“. American Journal of Applied sciences 03, Nr. 05 (31.05.2021): 245–50. http://dx.doi.org/10.37547/tajas/volume03issue05-39.

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Computer vision as a scientific discipline refers to the theories and technologies for creating artificial systems that receive information from an image. Despite the fact that this discipline is quite young, its results have penetrated almost all areas of life. Computer vision is closely related to other practical fields like image processing, the input of which is two-dimensional images obtained from a camera or artificially created. This form of image transformation is aimed at noise suppression, filtering, color correction and image analysis, which allows you to directly obtain specific information from the processed image. This information may include searching for objects, keypoints, segments, and annexes;

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G, Sankari, Pushpalatha G, Senbagam V, Karthika M und Anbuselvan N. „Search Engine Optimization in Advanced Computer Vision“. SIJ Transactions on Computer Networks & Communication Engineering 07, Nr. 01 (15.02.2019): 06–09. http://dx.doi.org/10.9756/sijcnce/v7i1/05020080102.

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M, Dhinakaran, Sivaranjani S, Sangeetha K, Thulasi A und Vinodhini R. „Advanced Computer Vision based Virtual Dressing Room“. SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 05, Nr. 04 (04.08.2017): 01–03. http://dx.doi.org/10.9756/sijcsea/v5i4/05010170101.

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Shao, Xinyu. „Review of computer vision in sports“. Applied and Computational Engineering 5, Nr. 1 (14.06.2023): 28–33. http://dx.doi.org/10.54254/2755-2721/5/20230519.

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All industries employ machine learning extensively, and one of the most promising fields is computer vision. Computer vision is a simulation of the human visual system that uses cameras and computers to take the role of the human eye to find the target, follow it, and gather data from it so that a decision may be made on whether to take further action or provide recommendations. The various uses of computer vision in sports are covered in this paper. Currently, computer vision is mostly utilized for broadcast enhancement, tracking and detection of players and balls. Although the games graphics has been substantially improved by this technology, there are still several flaws. For instance, some areas are not suited to employ this technology. Another is the issue of players being blocked in multiplayer sports. For broadcasters, computer vision has significant commercial value. For athletes, this technique can improve their performance.

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Raiturcar, Tanvi Poy. „Knowledge, Attitudes and Practices of Computer Vision Syndrome among Medical Students in Goa“. Epidemiology International 06, Nr. 01 (30.03.2021): 9–14. http://dx.doi.org/10.24321/2455.7048.202102.

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Introduction: There has been a tremendous increase in the use of computers and other screens by young adults in educational institutions for education, communication, and recreation. This can lead to computer vision syndrome. Computer vision syndrome includes a variety of symptoms faced by individuals who use computers for long hours every day. Most early symptoms are not recognized and the condition goes undiagnosed. Creating public awareness about the healthy use of computers is the need of the hour. Aim: To study knowledge, attitudes and practices of computer vision syndrome among medical students in Goa. Methods: Settings and Design: Cross-sectional descriptive study. Study Duration: 1 month (June 2020) Statistical Analysis Tools Used: Simple percentages and proportions. Result: It is seen that among participants who use digital devices for more than 6 hours, 39 (92.9%) were symptomatic. 62 (57.4%) participants experienced worsening of symptoms due to lockdown. Conclusion: The present study revealed that more than three-fourths of the students complained of one or more symptoms of computer vision syndrome while working on the devices.

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Raiturcar, Tanvi Poy. „Knowledge, Attitudes and Practices of Computer Vision Syndrome among Medical Students in Goa“. Epidemiology International 06, Nr. 01 (30.03.2021): 9–14. http://dx.doi.org/10.24321/2455.7048.202102.

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Introduction: There has been a tremendous increase in the use of computers and other screens by young adults in educational institutions for education, communication, and recreation. This can lead to computer vision syndrome. Computer vision syndrome includes a variety of symptoms faced by individuals who use computers for long hours every day. Most early symptoms are not recognized and the condition goes undiagnosed. Creating public awareness about the healthy use of computers is the need of the hour. Aim: To study knowledge, attitudes and practices of computer vision syndrome among medical students in Goa. Methods: Settings and Design: Cross-sectional descriptive study. Study Duration: 1 month (June 2020) Statistical Analysis Tools Used: Simple percentages and proportions. Result: It is seen that among participants who use digital devices for more than 6 hours, 39 (92.9%) were symptomatic. 62 (57.4%) participants experienced worsening of symptoms due to lockdown. Conclusion: The present study revealed that more than three-fourths of the students complained of one or more symptoms of computer vision syndrome while working on the devices.

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Murthy, G. R. S., und R. S. Jadon. „Computer Vision Based Human Computer Interaction“. Journal of Artificial Intelligence 4, Nr. 4 (15.09.2011): 245–56. http://dx.doi.org/10.3923/jai.2011.245.256.

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Maxwell, Bruce A. „Teaching Computer Vision to Computer Scientists“. International Journal of Pattern Recognition and Artificial Intelligence 12, Nr. 08 (Dezember 1998): 1035–51. http://dx.doi.org/10.1142/s0218001498000580.

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Computer vision is a broad-based field of computer science that requires students to understand and integrate knowledge from numerous disciplines. Computer science (CS) majors, however, do not necessarily have an interdisciplinary background. In the rush to integrate, we can forget, or fail to plan for the fact that our students may not possess a broad undergraduate education. To explore the appropriateness of our education materials, this paper begins with a discussion of what we can expect CS majors to know and how we can use that knowledge to make a computer vision course a more enriching experience. The paper then provides a review of a number of the currently available computer vision textbooks. These texts differ significantly in their coverage, scope, approach, and audience. This comparative review shows that, while there are an increasing number of good textbooks available, there is still a need for new educational materials. In particular, the field would benefit from both an undergraduate computer vision text aimed at computer scientists and from a text with a stronger focus on color computer vision and its applications.

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Freeman, W. T., D. B. Anderson, P. Beardsley, C. N. Dodge, M. Roth, C. D. Weissman, W. S. Yerazunis et al. „Computer vision for interactive computer graphics“. IEEE Computer Graphics and Applications 18, Nr. 3 (1998): 42–53. http://dx.doi.org/10.1109/38.674971.

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Indhurani, A., A. Manimegalai, I. Arunpandiyan, M. Ramachandran und Chinnasamy Sathiyaraj. „Exploring Recent Trends in Computer Vision“. Electrical and Automation Engineering 1, Nr. 1 (01.05.2022): 33–39. http://dx.doi.org/10.46632/eae/1/1/6.

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Computer vision is Artificial Intelligence (AI) is a field of digital imagery, recovering meaningful information from videos and other visual inputs helps computers and systems - and take action or make recommendations based on that information. The main purpose of animal ecology is to observe living things in the natural world. The cost and challenge of data collection often restricts the scope of ecological research. Clinical image analysis involves the development, classification and diagnosis of a clinical picture. Computer Vision Syndrome (CVS) is a set of symptoms associated with prolonged work on a computer display. The answers to the diagnostic features can be displayed as thermo grams in leaf pictures, the answers to the diagnostic features can be displayed as thermo grams in leaf pictures, systematic and pale with the help of computer vision plenty for botanical studies the leaves are ready to make new contributions.

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Sitaula, Kanchan, Navaraj Kafle, Aashish Acharya und Ved Prakash Mishra. „Prevalence and associated factors of computer vision syndrome among the computer engineering students of Pokhara University affiliated colleges of Kathmandu valley“. International Journal Of Community Medicine And Public Health 7, Nr. 6 (27.05.2020): 2027. http://dx.doi.org/10.18203/2394-6040.ijcmph20202448.

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Background: The increasing use of computers and electronic devices is rapidly increasing the related health issues of computer vision syndrome. Studies have identified longer use of computers, ergonomic practices as lighting condition of room, incorrect distance between eye and computer, refresh rate, use of spectacles were associated with computer vision syndrome (CVS) symptoms as back pain, tension, headache and others. The objective of this study was to find out the prevalence of CVS among computer engineering students of Pokhara University affiliated colleges of Kathmandu Valley and identify the associated factors and preventive measures being practiced by the students.Methods: A cross-sectional descriptive study was carried out using self-administered questionnaire among 234 undergraduate computer engineering students of Kathmandu Valley. Chi-square test was used to identify the association with computer vision syndrome and its determinants.Results: The prevalence of computer vision syndrome among the computer engineering students was found to be 76.50%. Only 39.3% were found to be using computer in upright with straight back posture and 73.5% were using computer at distance less than or equal to 50 cm. The 81.2% of participants were not following the 20/20/20 rule. During age, use of vision aid lens and use of protective eye glasses and artificial eye drops were found associated with CVS.Conclusions:The study revealed that the prevalence of computer vision syndrome was significantly high. Individuals using vision aid lens were found to be at risk of developing CVS and use of protective eye glass and artificial tears were found protective.

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Prijs, Jasper, Zhibin Liao, Soheil Ashkani-Esfahani, Jakub Olczak, Max Gordon, Prakash Jayakumar, Paul C. Jutte, Ruurd L. Jaarsma, Frank F. A. IJpma und Job N. Doornberg. „Artificial intelligence and computer vision in orthopaedic trauma“. Bone & Joint Journal 104-B, Nr. 8 (01.08.2022): 911–14. http://dx.doi.org/10.1302/0301-620x.104b8.bjj-2022-0119.r1.

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Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the ‘why’), the current applications (the ‘what’), and the approach to unlocking its full potential (the ‘how’). Cite this article: Bone Joint J 2022;104-B(8):911–914.

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Pulla, Anupama, Asma ., Nanduri Samyuktha, Soumya Kasubagula, Aishwarya Kataih, Devender Banoth und Harshitha Addagatla. „A cross sectional study to assess the prevalence and associated factors of computer vision syndrome among engineering students of Hyderabad, Telangana“. International Journal Of Community Medicine And Public Health 6, Nr. 1 (24.12.2018): 308. http://dx.doi.org/10.18203/2394-6040.ijcmph20185264.

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Background: Computer vision syndrome is a complex of eye and vision problems related to near work which are experienced due to prolonged computer use. Computers demand near work, for longer duration which increases discomfort of eye and prolonged exposure to the discomfort leads to a cascade of symptoms that can be referred as computer vision syndrome. The aims and objectives of the study were to determine the prevalence of computer vision syndrome among engineering students of Hyderabad, Telangana and to determine the factors related to computer vision syndrome among study population.Methods: A cross sectional study was carried out from March to June 2017 among students of an engineering college in Hyderabad, Telangana. A convenient sample of 300 students was taken and a predesigned, pre tested questionnaire was used to obtain information.Results: Majority of study population were males (56.3%). Around 75.1% of study population were using all the electronic gadgets like computers, laptops and smartphones. The prevalence of computer vision syndrome was found to be 60.3%. Around 46.7% of study population viewed the screen from a distance of 22-40 centimeters.Conclusions: As students pursuing engineering stream are the future IT and Computer software engineers, preventive strategies adopted by them will significantly decrease the burden of computer vision syndrome and improve productivity. In this study a significant proportion of the engineering students were found to be having vision problems, which emphasizes the need to adopt preventive measures to avoid computer vision syndrome.

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Mallula, Surya Kiran, und Janagama Sravanthi. „Computer Vision Syndrome and Scope of Homoeopathy“. International Journal of Research and Review 10, Nr. 8 (18.08.2023): 523–27. http://dx.doi.org/10.52403/ijrr.20230868.

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The utilization of technology has become a fundamental part of our daily lives. Therefore, regardless of whether one is an adult or a child, there are various motivations for individuals to remain attached to their digital devices. As the utilization of computers and video display terminals in the workplace increases, a growing number of individuals are experiencing symptoms associated with prolonged use of computers. These symptoms encompass visual and eye symptoms, as well as musculoskeletal symptoms, and are collectively referred to as Computer Vision Syndrome (CVS). There are a variety of allopathic treatments available, however, they are limited in their long-term use and associated side effects. Establishing an ergonomically designed work environment is an important factor in reducing the risk of developing CVS symptoms, and alternative treatments may be appropriate depending on the underlying causes of CVS and associated symptoms. Homeopathy is an alternative system of medicine that allow the court to choose the indicated homoeopathic medicine for management of computer vision syndrome. Keywords: Homeopathy, computer vision syndrome (cvs), Digital eyestrain (DES), Video display terminals (VDTs),

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Bridger, Mark. „Calculus and Computer Vision“. College Mathematics Journal 23, Nr. 2 (März 1992): 132. http://dx.doi.org/10.2307/2686673.

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Kovashka, Adriana, Olga Russakovsky, Li Fei-Fei und Kristen Grauman. „Crowdsourcing in Computer Vision“. Foundations and Trends® in Computer Graphics and Vision 10, Nr. 3 (2016): 177–243. http://dx.doi.org/10.1561/0600000071.

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Rode, Jörg. „Computer Vision statt Kasse“. Lebensmittel Zeitung 73, Nr. 52 (2021): 20. http://dx.doi.org/10.51202/0947-7527-2021-52-020-4.

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Cheng, Wen-Huang, Sijie Song, Chieh-Yun Chen, Shintami Chusnul Hidayati und Jiaying Liu. „Fashion Meets Computer Vision“. ACM Computing Surveys 54, Nr. 4 (Juli 2021): 1–41. http://dx.doi.org/10.1145/3447239.

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Fashion is the way we present ourselves to the world and has become one of the world’s largest industries. Fashion, mainly conveyed by vision, has thus attracted much attention from computer vision researchers in recent years. Given the rapid development, this article provides a comprehensive survey of more than 200 major fashion-related works covering four main aspects for enabling intelligent fashion: (1) Fashion detection includes landmark detection, fashion parsing, and item retrieval; (2) Fashion analysis contains attribute recognition, style learning, and popularity prediction; (3) Fashion synthesis involves style transfer, pose transformation, and physical simulation; and (4) Fashion recommendation comprises fashion compatibility, outfit matching, and hairstyle suggestion. For each task, the benchmark datasets and the evaluation protocols are summarized. Furthermore, we highlight promising directions for future research.