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Journal articles on the topic 'Computer vision technology'

Author: Grafiati

Published: 11 December 2022

Last updated: 27 January 2023

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1

Ohta, Yuichi. "3D Image Media and Computer Vision -From CV as Robot Technology to CV as Media Technology-." Journal of Robotics and Mechatronics 9, no. 2 (April 20, 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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Manasi, Mohaney, and Sonal Chaudhary. "Computer Vision Technology using Gesture Recognition." International Journal of Computer Applications 179, no. 19 (February 15, 2018): 1–4. http://dx.doi.org/10.5120/ijca2018916320.

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Milella, A., G. Reina, and M. Foglia. "Computer vision technology for agricultural robotics." Sensor Review 26, no. 4 (October 2006): 290–300. http://dx.doi.org/10.1108/02602280610692006.

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Ramadoss, Janarthanan, J. Venkatesh, Shubham Joshi, Piyush Kumar Shukla, Sajjad Shaukat Jamal, Majid Altuwairiqi, and Basant Tiwari. "Computer Vision for Human-Computer Interaction Using Noninvasive Technology." Scientific Programming 2021 (November 3, 2021): 1–15. http://dx.doi.org/10.1155/2021/3902030.

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Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.

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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.

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Niu, Xiang Jie, and Bin Lan. "The Agricultural Products Deterioration Recognition Based on Computer Vision." Applied Mechanics and Materials 602-605 (August 2014): 2027–30. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2027.

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The computer vision technology is an important branch of computer science and artificial intelligence which is regarded as a non-destructive testing technique in the field of agriculture with a broad application prospects. This paper introduces the application of the computer vision technology in the agricultural products deterioration recognition, builds foundations for the accurate measurement of the agricultural products quality with computer visions, and establish the relationship between the feature information and quality of the agricultural products. Meanwhile, this paper combined the computer vision technology with infrared, microwave, NMR techniques to extract and test the visual information of the internal quality of the agricultural products.

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Mery, D. "Computer vision technology for X-ray testing." Insight - Non-Destructive Testing and Condition Monitoring 56, no. 3 (March 1, 2014): 147–55. http://dx.doi.org/10.1784/insi.2014.56.3.147.

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Luo, Bo, Xudong Wang, and Zhiyuan Zhang. "Application of Computer Vision Technology in UAV." Journal of Physics: Conference Series 1881, no. 4 (April 1, 2021): 042052. http://dx.doi.org/10.1088/1742-6596/1881/4/042052.

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9

Yang, Zeping, and Ge Gao. "Audit Optimization Based on Computer Vision Technology." Journal of Computer and Communications 10, no. 10 (2022): 50–58. http://dx.doi.org/10.4236/jcc.2022.1010004.

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Floryanzia, Sydney, Preethi Ramesh, Madeline Mills, Sanjana Kulkarni, Grace Chen, Prashant Shah, and David Lavrich. "Disintegration testing augmented by computer Vision technology." International Journal of Pharmaceutics 619 (May 2022): 121668. http://dx.doi.org/10.1016/j.ijpharm.2022.121668.

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11

Shi, Jing, Xiangjun Xu, Jialai Wang, and Gong Li. "Beam damage detection using computer vision technology." Nondestructive Testing and Evaluation 25, no. 3 (September 2010): 189–204. http://dx.doi.org/10.1080/10589750903242525.

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12

Gunasekaran, Sundaram. "Computer vision technology for food quality assurance." Trends in Food Science & Technology 7, no. 8 (August 1996): 245–56. http://dx.doi.org/10.1016/0924-2244(96)10028-5.

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Ya-ming, Wang, Huang Wen-qing, and Zhao Yun. "Computer vision technology in log volume inspection." Journal of Forestry Research 13, no. 1 (March 2002): 67–70. http://dx.doi.org/10.1007/bf02857149.

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14

Chen, Biao, and Yuzhe Liu. "Antenna alignment by using computer vision technology." Microwave and Optical Technology Letters 62, no. 3 (October 24, 2019): 1267–69. http://dx.doi.org/10.1002/mop.32126.

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Zhang, Yupeng, and Gaowei Zhao. "Conservative Treatment and Rehabilitation Training for Rectus Femoris Tear in Basketball Training Based on Computer Vision." Applied Bionics and Biomechanics 2022 (May 5, 2022): 1–12. http://dx.doi.org/10.1155/2022/6230025.

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Computer vision is an emerging artificial intelligence subject, whose purpose is to make computers have the same ability to perceive and understand image semantic information as humans. Computer vision technology is based on high-performance computers, which can obtain massive amounts of information and data in a short period of time and use intelligent algorithms to perform high-speed data processing on the information, which is conducive to the integration of information related to product design, production process management, etc. Due to the rapid development of visual sensing technology, computer technology, and image processing technology, computer vision technology has been widely used in the fields of food, medicine, construction, chemical industry, electronics, packaging, and automobiles. This article uses computer vision technology to compare four conservative treatments and rehabilitation training for rectus femoris in basketball training and analyze the best rehabilitation treatment for rectus femoris tear. The experimental results show that the average electroacupuncture plus muscle stretching exercise group after treatment has an average EMG value of 55.49, an average muscle strength rating of five, an average motor function score of 23.45, and an average treatment recovery time of 11.6 days. This group has the best treatment effect.

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Wen, K. X., Z. M. Xie, L. M. Yang, B. Q. Sun, J. H. Wang, and Q. Sun. "Computer vision technology determines optimal physical parameters for sorting JinDan 73 maize seeds." Seed Science and Technology 43, no. 1 (April 28, 2015): 62–70. http://dx.doi.org/10.15258/sst.2015.43.1.07.

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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, and Job N. Doornberg. "Artificial intelligence and computer vision in orthopaedic trauma." Bone & Joint Journal 104-B, no. 8 (August 1, 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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Zhang, Fang, and Li Si Fu. "Application of Computer Vision Technology in Agricultural Field." Applied Mechanics and Materials 462-463 (November 2013): 72–76. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.72.

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Computer vision technology has been widely applied to various fields of agricultural development, and with the rapid development of computer technology, graphics and image processing technology, enormous progress has been achieved on its applications in agriculture. This paper has reviewed and summarized the development of computer vision technology mainly in detection and grading of the quality of agricultural products, crop monitoring, automation of agricultural production, crop disease identification and other aspects and has discussed the prospect for future development.

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Gao, Fa Zhao. "The Simulation of the Psychological Impact of Computer Vision De-Noising Technology." Applied Mechanics and Materials 556-562 (May 2014): 5013–16. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5013.

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The paper mainly discusses the analysis method for the psychological impact of computer vision noising technology. Actually, people's psychological acceptance and corresponding memory capacity of computer vision images with lots of noise are relatively poor. The de-noising process to computer vision images can improve the clarity, thus generating passive psychological impact. Therefore, the paper proposes a spatial domain filtering algorithm-based de-noising method for computer vision. It establishes wavelet packet decomposition tree for computer vision images and de-noises accordance with the decomposing results. The experiment results show that the proposed de-noising method has passive psychological influence and improves the memory capacity of computer vision images.

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Gao, Yi. "Research on the Application of Artificial Intelligence Technology in the Development of Computer Vision." Highlights in Science, Engineering and Technology 9 (September 30, 2022): 80–84. http://dx.doi.org/10.54097/hset.v9i.1720.

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Nowadays, the computer field is in the stage of vigorous development. In recent years, with the emergence of Artificial Intelligence (AI), more and more people have taken a keen interest in it and conducted in-depth research on it. As an important branch of AI, computer vision aims to make machines have vision similar to that of human beings. Computer vision has gradually changed from the previous recognition of computer pictures to the recognition of real life, thus taking an important step in technological development. Because computer vision technology is a comprehensive technology that includes many disciplines and can obtain complete information from images, computer vision technology has been applied in various fields. However, there are a series of problems in the development of computer vision, such as the difficulty in extracting features or information from complex scenes. This paper analyzes the related theories of AI and computer vision technology, and discusses the application and prospect of AI technology in the development of computer vision.

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Feiyu, Guo, He Yuanjun, Dong Yinfeng, and Li Yutong. "Structure modal identification based on computer vision technology." Vibroengineering PROCEDIA 37 (May 7, 2021): 72–77. http://dx.doi.org/10.21595/vp.2021.21945.

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22

Zhao, Ping, Yong Kui Li, and Shan Liang Xie. "Composite Measurement Method Based on Computer Vision Technology." Advanced Materials Research 418-420 (December 2011): 2118–21. http://dx.doi.org/10.4028/www.scientific.net/amr.418-420.2118.

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Based on analyzing current methods of acquiring point clouds data in reversing engineering, considering the features of structure light scanning and binocular stereovision, we proposed a composite measuring method based on vision technology, which combines the advantages of the two measurement methods in together, and established the measuring system. The greatest advantage of this method is that the edge data were obtained by binocular vision technology, and the internal data were obtained by Structure light method. In binocular vision, stereo matching was realized with the combination of epipolar constraint, corner feature matching and area matching, which could improve location precision and matching speed of feature point. An application example shows the composite measuring method is feasible.

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Rahman, Md Faizur, Abdullah Iqbal, Md Abul Hashem, and Akinbode A. Adedeji. "Quality Assessment of Beef Using Computer Vision Technology." Food Science of Animal Resources 40, no. 6 (November 2020): 896–907. http://dx.doi.org/10.5851/kosfa.2020.e57.

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24

Cheng, Qian. "Application of Computer Vision Technology in Industrial Automation." Journal of Physics: Conference Series 2037, no. 1 (September 1, 2021): 012015. http://dx.doi.org/10.1088/1742-6596/2037/1/012015.

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25

Seng, Kah Phooi, Li-Minn Ang, Leigh M. Schmidtke, and Suzy Y. Rogiers. "Computer Vision and Machine Learning for Viticulture Technology." IEEE Access 6 (2018): 67494–510. http://dx.doi.org/10.1109/access.2018.2875862.

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Romachev, Artem, Valentin Kuznetsov, Egor Ivanov, and Benndorf Jörg. "Flotation froth feature analysis using computer vision technology." E3S Web of Conferences 192 (2020): 02022. http://dx.doi.org/10.1051/e3sconf/202019202022.

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The possibility of machine vision application in the field of flotation efficiency evaluation was studied. Algorithm for froth image analysis was developed with aim of obtaining bubble’s size distribution. Algorithm consists of two parts: image processing and object detection. Algorithm’s work was verified on the sulfide flotation froth. As result, mathematical correlations for air flow rate, mean bubble diameter and surface area bubble flux were established.

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Tian, Hongkun, Tianhai Wang, Yadong Liu, Xi Qiao, and Yanzhou Li. "Computer vision technology in agricultural automation —A review." Information Processing in Agriculture 7, no. 1 (March 2020): 1–19. http://dx.doi.org/10.1016/j.inpa.2019.09.006.

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Zhang, Wei, Bo Cheng, and Yingzi Lin. "Driver drowsiness recognition based on computer vision technology." Tsinghua Science and Technology 17, no. 3 (June 2012): 354–62. http://dx.doi.org/10.1109/tst.2012.6216768.

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Meng, Chai Hong, Mien May Chong, and Chandra Reka Ramachandiran. "Incorporate Computer Vision Technology into Mobile Application to Facilitate the Movement of Visually Impaired." Webology 18, SI05 (October 30, 2021): 738–50. http://dx.doi.org/10.14704/web/v18si05/web18258.

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In 2021, visually impairment is still a serious problem in the world and there are no immediate and efficient ways of solving it. Hence, the purpose of this project is to develop a mobile application that act as a visual aid tool for the visually impaired people. Based on research, visually impaired people tend to depend other senses, for example, touching and listening to gather sufficient information for them to identify environment they are in and understand objects that are around them. Therefore, the opportunity is taken to conduct a research based on the purpose of this project, in order to improve accuracy of senses for human by using computer vision technology, helping visually impaired user to recognize and identify objects, texts and colours through mobile camera and transforming the information into auditory signal. Surveys and interviews were conducted on this project to identify and analyse the utility of the proposed technologies and application for normal and visually impaired users. Research findings indicate that the proposed application has the potential to improve the lives of people with visual impairments, and eliminate traditional unsafe practices.

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Jayakumar, Gayatri G., Bindu Thampi, Mahadevan K. Iyer, and Rekha R. Sasidharan. "Awareness of computer vision syndrome and related factors among information technology professionals." International Journal of Research in Medical Sciences 8, no. 12 (November 27, 2020): 4336. http://dx.doi.org/10.18203/2320-6012.ijrms20205301.

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Background: Extensive use of computers for both professional and personal purpose has led to an increase in computer related health problems. Aim of this study was to evaluate the awareness regarding symptoms and the related factors in computer vision syndrome (CVS) among a group of information technology (IT) professionals.Methods: This study was conducted among 300 IT employees. After obtaining informed consent, structured questionnaire was given and data collected and analyzed.Results: Out of 300 respondents, 194 (64.7%) were males and 106 (35.3%) females. Mean age was 30.5 years.48.3% had more than eight years of computer usage and 77.3% used computers on an average of 8-12 hours per day. About 201 (67%) of the participants had at least one ocular symptom, most common was dryness (96%). 94.7% reported eyestrain and 86.3% reported redness and itching. Dryness and eyestrain were more in males (p<0.05). Dryness, eyestrain and redness were more in those who used computers for more than eight years duration and for 8-12 hours/day (p<0.05). 253 (84.3%) were aware about this syndrome, the main source being internet. The main relief measure adopted was to take a break in between the work hours. Only 51.3% consulted a doctor for their symptoms. Conclusions: In our study though 84.3% of IT professionals were aware of CVS and 67% had at least one ocular symptom, only 51.3% took professional advice for their problems. There is a definite need for awareness about corrective measures and treatment methods to be adopted for CVS among IT professionals.

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Ramezani Dooraki, Amir. "A survey on computer vision technology in Camera Based ETA devices." International Journal of Advances in Intelligent Informatics 1, no. 3 (November 30, 2015): 115. http://dx.doi.org/10.26555/ijain.v1i3.40.

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Electronic Travel Aid systems are expected to make impaired persons able to perform their everyday tasks such as finding an object and avoiding obstacles easier. Among ETA devices, Camera Based ETA devices are the new one and with a high potential for helping Visually Impaired Persons. With recent advances in computer science and specially computer vision, Camera Based ETA devices used several computer vision algorithms and techniques such as object recognition and stereo vision in order to help VIP to perform tasks such as reading banknotes, recognizing people and avoiding obstacles. This paper analyses and appraises a number of literatures in this area with focus on stereo vision technique. Finally, after discussing about the methods and techniques used in different literatures, it is concluded that the stereo vision is the best technique for helping VIP in their everyday navigation.

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Al Smadi, Takialddin. "Modern Technology for Image processing and Computer vision -A Review." Journal of advanced Sciences and Engineering Technologies 1, no. 2 (May 21, 2018): 17–23. http://dx.doi.org/10.32441/jaset.v1i2.178.

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This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality. © 2018 JASET, International Scholars and Researchers Association

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Li, Hong Zhou, Jie Lu, Jun Wang, and Tao Jia. "The Tape Measurement System Based on Computer Vision Technology." Applied Mechanics and Materials 738-739 (March 2015): 816–19. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.816.

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The paper devises a computer vision system based on virtual instruments to measure tape. The dark field illumination is chosen as Lampe-house in this system. Use Image processing technologies of Median filter, Image binarization, Template matching, Edge extraction to extract a reticle of tape. And compare it with standard tape enacted in this system to measure reticle error of the tape. Analyze various factors influencing the detection precision of the system. Tests show that the measurement results of this system are accurate, reliable and practicable.

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Tanigawa, Tohru, Toshitsugu Sawai, and Tadashi Nakao. "High Precision Automatic Alignment and its Computer Vision Technology." Journal of Robotics and Mechatronics 1, no. 3 (October 20, 1989): 220–26. http://dx.doi.org/10.20965/jrm.1989.p0220.

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Recently, industrial robotics and computer vision technology has become very important in flexible manufacturing systems and automated factories. Especially high precision automatic alignment technology beyond human ability is essential to some manufactures, and its application fields are extending rapidly. This paper describes the high precision automatic alignment system of large-sized LCD panels. The features of the system are (1) high precision and high speed detection of position using the special alignment mark, (2) high contrast image obtained by the use of ultraviolet rays, (3) new image-processing algorithms for improvement of system reliability.

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Singh, Amit, Albert Haque, Alexandre Alahi, Serena Yeung, Michelle Guo, Jill R. Glassman, William Beninati, Terry Platchek, Li Fei-Fei, and Arnold Milstein. "Automatic detection of hand hygiene using computer vision technology." Journal of the American Medical Informatics Association 27, no. 8 (July 26, 2020): 1316–20. http://dx.doi.org/10.1093/jamia/ocaa115.

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Abstract Objective Hand hygiene is essential for preventing hospital-acquired infections but is difficult to accurately track. The gold-standard (human auditors) is insufficient for assessing true overall compliance. Computer vision technology has the ability to perform more accurate appraisals. Our primary objective was to evaluate if a computer vision algorithm could accurately observe hand hygiene dispenser use in images captured by depth sensors. Materials and Methods Sixteen depth sensors were installed on one hospital unit. Images were collected continuously from March to August 2017. Utilizing a convolutional neural network, a machine learning algorithm was trained to detect hand hygiene dispenser use in the images. The algorithm’s accuracy was then compared with simultaneous in-person observations of hand hygiene dispenser usage. Concordance rate between human observation and algorithm’s assessment was calculated. Ground truth was established by blinded annotation of the entire image set. Sensitivity and specificity were calculated for both human and machine-level observation. Results A concordance rate of 96.8% was observed between human and algorithm (kappa = 0.85). Concordance among the 3 independent auditors to establish ground truth was 95.4% (Fleiss’s kappa = 0.87). Sensitivity and specificity of the machine learning algorithm were 92.1% and 98.3%, respectively. Human observations showed sensitivity and specificity of 85.2% and 99.4%, respectively. Conclusions A computer vision algorithm was equivalent to human observation in detecting hand hygiene dispenser use. Computer vision monitoring has the potential to provide a more complete appraisal of hand hygiene activity in hospitals than the current gold-standard given its ability for continuous coverage of a unit in space and time.

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Voigt, Holger, and Richarda Classen. "Computer vision and digital imaging technology in melanoma detection." Seminars in Oncology 29, no. 4 (August 2002): 308–27. http://dx.doi.org/10.1053/sonc.2002.34109.

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Falco, Charles M. "Computer Vision and Art." IEEE Multimedia 14, no. 2 (April 2007): 8–11. http://dx.doi.org/10.1109/mmul.2007.31.

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Wang, Peng. "Research on Sports Training Action Recognition Based on Deep Learning." Scientific Programming 2021 (June 29, 2021): 1–8. http://dx.doi.org/10.1155/2021/3396878.

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With the rapid development of science and technology in today’s society, various industries are pursuing information digitization and intelligence, and pattern recognition and computer vision are also constantly carrying out technological innovation. Computer vision is to let computers, cameras, and other machines receive information like human beings, analyze and process their semantic information, and make coping strategies. As an important research direction in the field of computer vision, human motion recognition has new solutions with the gradual rise of deep learning. Human motion recognition technology has a high market value, and it has broad application prospects in the fields of intelligent monitoring, motion analysis, human-computer interaction, and medical monitoring. This paper mainly studies the recognition of sports training action based on deep learning algorithm. Experimental work has been carried out in order to show the validity of the proposed research.

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Farhat, Mrs Naziya, and Mohammad Nasiruddin. "Computer Vision based Surveillance System." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1491–95. http://dx.doi.org/10.22214/ijraset.2022.41512.

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Abstract: Automated surveillance systems are surveillance systems that employ cameras to monitor the area. The observed setting is analyzed using motion detection, crowd behavior, individual behavior, and interactions between people, crowds, and their surroundings. These automated systems are capable of a wide range of tasks, including detection, interpretation, understanding, recording, and alert creation based on the results of the study. Researchers have been able to increase monitoring performance while limiting human errors by changing various features of these systems. This research looks into video surveillance systems and the components that go with them in depth. The designs that were used, as well as the most important studies in these systems, are presented. Existing surveillance systems were compared in terms of to provide a wider picture and a more holistic understanding of the system.

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Wang, Xianghan, Jie Jiang, Yingmei Wei, Lai Kang, and Yingying Gao. "Research on Gesture Recognition Method Based on Computer Vision." MATEC Web of Conferences 232 (2018): 03042. http://dx.doi.org/10.1051/matecconf/201823203042.

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Gesture recognition is an important way of human-computer interaction. With time going on, people are no longer satisfied with gesture recognition based on wearable devices, but hope to perform gesture recognition in a more natural way. Computer vision-based gesture recognition can transfer human feelings and instructions to computers conveniently and efficiently, and improve the efficiency of human-computer interaction significantly. The gesture recognition based on computer vision is mainly based on hidden Markov, dynamic time rounding algorithm and neural network algorithm. The process is roughly divided into three steps: image collection, hand segmentation, gesture recognition and classification. This paper reviews the computer vision-based gesture recognition methods in the past 20 years, analyses the research status at home and abroad, summarizes its current development, the advantages and disadvantages of different gesture recognition methods, and looks forward to the development trend of gesture recognition technology in the next stage.

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Li, Nan. "Metal Jewelry Craft Design Based on Computer Vision." Computational Intelligence and Neuroscience 2022 (June 15, 2022): 1–11. http://dx.doi.org/10.1155/2022/3843421.

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Combining computer vision technology with process design, a new design and production method is obtained, which breaks through the limitations of traditional jewelry creation and provides new possibilities for the realization of complex jewelry structures. When technology no longer becomes the bottleneck of artistic expression, the space of art will be greatly expanded. Science and technology leading design method has become a new way to assist jewelry artists in subjective creation. According to various thoughts and ideas in design, establishing the corresponding algorithm rules and parameters can generate the scheme through calculation. The design result obtained in this way not only has a scientifically logical basis but also obtains the result beyond the normal imagination space due to the intelligent design process. This paper tries to apply computer vision technology to modern jewelry design, analyzes several aspects of computer vision application in process design, and combines the latest technical means to put forward algorithms for verification. The results prove that computer vision can improve the efficiency of crafts design significantly.

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Fadillah Tarigan, Muhammad, and Rina Amelia. "The Relation Between Vision Distance, Body Posture, And Long Use of Computer with Computer Vision Syndrome in Faculty of Medicine Universitas Sumatera Utara." KESANS : International Journal of Health and Science 1, no. 5 (February 20, 2022): 507–13. http://dx.doi.org/10.54543/kesans.v1i5.48.

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Background: The COVID-19 pandemic affects all activities of human life, including education. Educational activities that are usually carried out face-to-face in class have now been stopped to avoid the expansion of the spread of COVID-19. The cessation of face-to-face learning activities is shifted to online-based learning (distance) by utilizing technology that is connected to the internet. This distance learning system applies to all levels of education. Learning with an online system using a computer all day makes various complaints experienced by students such as computer vision syndrome. The most common eye complaints condition is computer vision syndrome, some eye and vision problems that originate from prolonged use of computers, tablet computers, electronic readers, and mobile phones. Computer Vision Syndrome is a collection of symptoms in the eyes and neck caused by excessive use of computers/monitors. Symptoms vary but mostly involve eye strain, headache, blurred vision, dry eyes, irritation, slowed focusing of the eyes, pain in the neck, back, and sensitivity to light. Objective: The purpose of this study was to analyze the relationship between visibility, body posture, and duration of computer use with Computer Vision Syndrome (CVS) in USU Medical Faculty students Methods: This type of research is analytic with a cross-sectional study approach. The sample of this study amounted to 100 students who were randomly selected using stratified random sampling. The data used is primary data from questionnaires filled out online through the Line, WhatsApp, and Instagram applications. Data analysis using SPSS with chi-square statistical test (p <0.05). Results: The results showed that the most visibility was < 50 c, frequent bad posture, and duration of use was > 4 hours. Conclusion: The results showed that there was a relationship between visibility, body posture, and duration of using a computer with CVS for USU Medical Faculty students.

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Obaidat, Mohammed Taleb, Hashem R. Al-Masaeid, Fouad Gharaybeh, and Taisir S. Khedaywi. "An innovative digital image analysis approach to quantify the percentage of voids in mineral aggregates of bituminous mixtures." Canadian Journal of Civil Engineering 25, no. 6 (December 1, 1998): 1041–49. http://dx.doi.org/10.1139/l98-034.

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The objective of this study was to examine the feasibility of using a semiautomated computer-vision system to quantify the percentage of voids in mineral aggregates (VMA%) of bituminous mixtures. The system used a hybrid procedure which utilized a digital image analysis scheme and a planimeter surveying instrument. Thirty-nine Marshall specimens were prepared using limestone and gravel aggregates. Values of VMA% were obtained using the ASTM conventional procedure and the computer-vision procedure. To compute VMA% using the computer-vision procedure, normal case photography with uniform scale images was used to map horizontal and vertical cross sections of Marshall specimens. Image domain measurements were corrected for distortion. Spatial filters and image processing operations were used to enhance the aggregate edges. Experimental results showed slight variations between VMA% computed using conventional and the computer-vision procedures. The average differences of VMA% between conventional and the computer-vision procedures were 0.81% and 0.006% for gravel and limestone specimens, respectively. Measurements of VMA% for limestone mixtures were more precise than those for gravel mixtures because of the angular edge shape of limestone particles. Variations in VMA% were due to the anisotropic properties of asphalt mixtures, aggregate distribution in the asphalt mixture, and different shapes of aggregates. Using the computer-vision-based technique, VMA% of horizontal and vertical cross sections were 50% consistent. The existence of fine aggregate in the asphalt mixture affected the accuracy potential of the developed system because a low-resolution camera was used. Increasing the camera resolution and automating the area computation of aggregate are expected to enhance the potential accuracy of the procedure. The proposed method for VMA quantification is anticipated to improve field quality control of hot-mix asphalt (HMA). The use of computer-vision technology with bituminous mixtures can open the doors to a wide variety of applications.Key words: bituminous mixtures, voids in mineral aggregate, computer vision, automation, image processing.

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Yamaguchi, Kota. "2. Computer Vision meets Fashion." Journal of The Institute of Image Information and Television Engineers 71, no. 11 (2017): 760–63. http://dx.doi.org/10.3169/itej.71.760.

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Agrawal, H., and A. Agrawal. "Human Computer Interaction congregate with computer vision A Review on Sixth Sense Technology." International Journal of Computer Sciences and Engineering 6, no. 5 (May 31, 2018): 887–91. http://dx.doi.org/10.26438/ijcse/v6i5.887891.

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Wang, Jun, and Xiao Hua Ni. "Angle Measurement Based on Computer Vision." Applied Mechanics and Materials 456 (October 2013): 115–19. http://dx.doi.org/10.4028/www.scientific.net/amm.456.115.

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In order to improve the precision and the speed of angle measurement,A new method for measuring the angle of the workpiece is presented in this paper, which is based on the computer vision testing technology. The image of workpiece is obtained, the first step is image preprocessing, then the measured worpiece image is processed by edge detection through Canny algorithm, specific features of workpieces edge is fully extracted, Then one can accomplish line detection by using Hough transform, Finally, the angle value is obtained through the means of Angle Calculation. By employing practical examples in engineering and simulation experiments, the experimental results proved the method has more strong anti-interference ability, more high accuracy and speed than traditional method.

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Silva, José Luís, Rui Bordalo, José Pissarra, and Paloma de Palacios. "Computer Vision-Based Wood Identification: A Review." Forests 13, no. 12 (November 30, 2022): 2041. http://dx.doi.org/10.3390/f13122041.

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Wood identification is an important tool in many areas, from biology to cultural heritage. In the fight against illegal logging, it has a more necessary and impactful application. Identifying a wood sample to genus or species level is difficult, expensive and time-consuming, even when using the most recent methods, resulting in a growing need for a readily accessible and field-applicable method for scientific wood identification. Providing fast results and ease of use, computer vision-based technology is an economically accessible option currently applied to meet the demand for automated wood identification. However, despite the promising characteristics and accurate results of this method, it remains a niche research area in wood sciences and is little known in other fields of application such as cultural heritage. To share the results and applicability of computer vision-based wood identification, this paper reviews the most frequently cited and relevant published research based on computer vision and machine learning techniques, aiming to facilitate and promote the use of this technology in research and encourage its application among end-users who need quick and reliable results.

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Park, Junseok, Youngbae Hwang, Ju-Hong Yoon, Min-Gyu Park, Jungho Kim, Yun Jeong Lim, and Hoon Jai Chun. "Recent Development of Computer Vision Technology to Improve Capsule Endoscopy." Clinical Endoscopy 52, no. 4 (July 31, 2019): 328–33. http://dx.doi.org/10.5946/ce.2018.172.

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Carpenter, Chris. "Computer Vision Technology Monitors Safety Zones and Automates Drillpipe Tally." Journal of Petroleum Technology 74, no. 05 (May 1, 2022): 70–72. http://dx.doi.org/10.2118/0522-0070-jpt.

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 208764, “A Better Set of Eyes: Computer Vision Technology To Monitor Safety Zones and Automate Drillpipe Tally,” by Douglas Watson, Helmerich and Payne; Kenneth Morton, CoVAR; and Sarah Kern, SPE, Helmerich and Payne, et al. The paper has not been peer reviewed. An indispensable item for every roughneck is the tally book, used to measure and count the drillpipe entering and exiting the wellbore. Traditional manual entry is prone to error, leading to potential mistakes in the calculated drilling depth and poorly sequenced lithologies. Computer vision technology has shown promise in other industries with its ability to automate similar recognition and counting tasks. A dual-use system has been developed in which the same cameras for pipe counting can be used for red-zone-entry detection, potentially enhance the safety of the drilling process. A Dual-Use System: Monitor Safety Zones and Automate Drillpipe Tally Drilling-depth measurement commonly is performed using the drillpipe itself, and it happens in two places. It is first measured using a tape measure or pipe strap as the pipe lies on the pipe delivery system (PDS) rack before it is loaded into the stands. Then, the pipe segments are counted above the rig floor as the pipes enter and exit the borehole. This count, combined with the pipe-tally measurements, is used to determine the depth of the hole. These two work locations—ground-level PDS and rig floor—also are areas where existing procedures are in place to reduce the likelihood of serious injury. In the case of the PDS area, where pipe lengths are measured, it is dangerous to enter any time the PDS is in operation. This dangerous red zone is defined as 45 ft on each side of the PDS. Numerous industry advances over the years have made the area safer. These methods, including buffer zones (creating an understood area not to be entered or to be avoided for personal safety) and barricades (a physical block or reminder to manage entry into a high-risk buffer zone), communicate visually the potential for hazardous situations. While proper inspection, planning, and buffer-zone and barricade procedure development does communicate the safety-critical nature of the PDS environment, the potential remains for dropped-object events and tubular-handling events that can cause significant injuries or fatalities.

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Arkhipova, M. Yu. "Modelling Crop Yield in Agricultural Regions Using Computer Vision Technology." Economy of Region 18, no. 2 (2022): 581–94. http://dx.doi.org/10.17059/ekon.reg.2022-2-20.

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The article examines new methodologies for modelling crop yield in agricultural regions of Russia based on the use of remote capabilities to get information on the field state. The proposed approach can be applied to develop indicator systems and create methodological platforms and models necessary to obtain more accurate estimates. In comparison with the traditional regression model, this method uses computer vision technology to gather additional data. Statistical hypothesis testing confirmed the significance of satellite photographs of fields for improving the accuracy of crop yield forecasting models. Traditional econometric tools were compared with various neural networks in order to discover the optimal model. The proposed tools were tested using data from 100 agricultural fields located in municipalities of 43 Russian regions, selected in proportion to the volume of crop production in this region. The conducted analysis showed the advantage of the mixed data neural network in comparison with other neural (multilayer perceptron and convolutional neural network) and regression models. In conditions of uncertainty and a large amount of data, the mixed data neural network can help obtain more accurate estimates. Additionally, while environmental factors have different effects on crop yields, they must be considered along with socio-economic characteristics. The use of new models and data types differing from table information can significantly improve the forecasting accuracy and interpretation. The analysis results can be used for examining and monitoring agricultural production in regional municipalities, determining farm resource requirements, as well as for creating sectoral and comprehensive projects and programmes for the development of the agricultural industry.

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