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Статті в журналах з теми "REAL TIME RECOGNITION"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

Anam, Sarawat, Shohidul Islam, M. A. Kashem, and M. A. Rahman. "Real Time Face Recognition Using Step Error Tolerance BPN." International Journal of Engineering and Technology 1, no. 1 (2009): 92–96. http://dx.doi.org/10.7763/ijet.2009.v1.17.

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2

Billings, Rachel M., and Alan J. Michaels. "Real-Time Mask Recognition." IoT 2, no. 4 (November 8, 2021): 688–716. http://dx.doi.org/10.3390/iot2040035.

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Анотація:

While a variety of image processing studies have been performed to quantify the potential performance of neural network-based models using high-quality still images, relatively few studies seek to apply those models to a real-time operational context. This paper seeks to extend prior work in neural-network-based mask detection algorithms to a real-time, low-power deployable context that is conducive to immediate installation and use. Particularly relevant in the COVID-19 era with varying rules on mask mandates, this work applies two neural network models to inference of mask detection in both live (mobile) and recorded scenarios. Furthermore, an experimental dataset was collected where individuals were encouraged to use presentation attacks against the algorithm to quantify how perturbations negatively impact model performance. The results from evaluation on the experimental dataset are further investigated to identify the degradation caused by poor lighting and image quality, as well as to test for biases within certain demographics such as gender and ethnicity. In aggregate, this work validates the immediate feasibility of a low-power and low-cost real-time mask recognition system.

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3

CRL Smetic Technology. "Real-time image recognition." NDT & E International 27, no. 1 (February 1994): 59–60. http://dx.doi.org/10.1016/0963-8695(94)90119-8.

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4

Patil, Anuradha, Chandrashekhar M. Tavade, and . "Methods on Real Time Gesture Recognition System." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 982. http://dx.doi.org/10.14419/ijet.v7i3.12.17617.

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Gesture recognition deals with discussion of various methods, techniques and concerned algorithms related to it. Gesture recognition uses a simple & basic sign languages like movement of hand, position of lips & eye ball as well as eye lids positions. The various methods for image capturing, gesture recognition, gesture tracking, gesture segmentation and smoothing methods compared, and by the overweighing advantage of different gesture recognitions and their applications. In recent days gesture recognition is widely utilized in gaming industries, biomedical applications, and medical diagnostics for dumb and deaf people. Due to their wide applications, high efficiency, high accuracy and low expenditure gestures are using in many applications including robotics. By using gestures to develop human computer interaction (HCI) method it is necessary to identify the proper and meaning full gesture from different gesture images. The Gesture recognition avoids use of costly hardware devices for understanding the activities and recognition example lots of I/O devices like keyboard mouse etc. Can be Limited.

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5

Y.Mohammed, Zaid, and Abdul Sattar M. Khidhir. "Real-Time Arabic Speech Recognition." International Journal of Computer Applications 81, no. 4 (November 15, 2013): 43–45. http://dx.doi.org/10.5120/14003-2048.

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6

Cetiner, Halit, Bayram Cetisli, and Ibrahim Cetiner. "Real Time Identification Number Recognition." SAÜ Fen Bilimleri Enstitüsü Dergisi 16, no. 2 (2012): 123–29. http://dx.doi.org/10.5505/saufbe.2012.18894.

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7

Albukhary, N., and Y. M. Mustafah. "Real-time Human Activity Recognition." IOP Conference Series: Materials Science and Engineering 260 (November 2017): 012017. http://dx.doi.org/10.1088/1757-899x/260/1/012017.

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8

MARIN, Florin-Bogdan, Gheorghe GURĂU, and Mihaela MARIN. "Real-Time Assembly Operation Recognition." Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science 45, no. 4 (December 15, 2022): 92–95. http://dx.doi.org/10.35219/mms.2022.4.15.

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Анотація:

This research is concerned to propose a computer vision algorithm to track manual assembly task. Manual assembly in case of electronics parts are used largely in automotive industry. The phases tracking of assembly could also be used for learning purposes such in case showed in this research, checking the assembly of an electronic educational board. The algorithms used for detection of different components are CNN (Convolutional Neuronal Network) as well as blob detection.

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9

Wu, Yi-Chang, Yao-Cheng Liu, and Ru-Yi Huang. "Real-time microreaction recognition system." IAES International Journal of Robotics and Automation (IJRA) 12, no. 2 (June 1, 2023): 157. http://dx.doi.org/10.11591/ijra.v12i2.pp157-166.

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Анотація:

<span lang="EN-US">This study constructed a real-time microreaction recognition system that can give real-time assistance to investigators. Test results indicated that the number of frames per second (30 or 190); angle of the camera, namely the front view of the interviewee or left (+45°) or right (−45°) view; and image resolution (480 or 680 p) did not have major effects on the system’s recognition ability. However, when the camera was placed at a distance of 300 cm, recognition did not always succeed. Value changes were larger when the camera was placed at an elevation 45° than when it was placed directly in front of the person being interrogated. Within a specific distance, the recognition results of the proposed real-time microreaction recognition system concurred with the six reaction case videos. In practice, only the distance and height of the camera must be adjusted in the real-time microreaction recognition system.</span>

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10

Shah, Dr Dipti M., and Parul D. Sindha. "Color detection in real time traffic sign detection and recognition system." Indian Journal of Applied Research 3, no. 7 (October 1, 2011): 152–53. http://dx.doi.org/10.15373/2249555x/july2013/43.

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11

Awwad, Sari, Sahar Idwan, and Hasan Gharaibeh. "Real-time sign languages character recognition." International Journal of Computer Applications in Technology 65, no. 1 (2021): 36. http://dx.doi.org/10.1504/ijcat.2021.113640.

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12

Gupta, Priyanshi, Amita Goel, Nidhi Sengar, and Vashudha Bahl. "HAND GESTURE RECOGNITION IN REAL-TIME." International journal of multidisciplinary advanced scientific research and innovation 1, no. 10 (December 25, 2021): 346–50. http://dx.doi.org/10.53633/ijmasri.2021.1.10.016.

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Анотація:

Hand gesture is language through which normal people can communicate with deaf and dumb people. Hand gesture recognition detects the hand pose and converts it to the corresponding alphabet or sentence. In past years it received great attention from society because of its application. It uses machine learning algorithms. Hand gesture recognition is a great application of human computer interaction. An emerging research field that is based on human centered computing aims to understand human gestures and integrate users and their social context with computer systems. One of the unique and challenging applications in this framework is to collect information about human dynamic gestures. Keywords: Tensor Flow, Machine learning, React js, handmark model, media pipeline

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13

Idwan, Sahar, Hasan Gharaibeh, and Sari Awwad. "Real-time sign languages character recognition." International Journal of Computer Applications in Technology 65, no. 1 (2021): 36. http://dx.doi.org/10.1504/ijcat.2021.10036093.

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14

Bordeaux, Theodore A. "Real time computer speech recognition system." Journal of the Acoustical Society of America 89, no. 3 (March 1991): 1489. http://dx.doi.org/10.1121/1.400618.

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15

Ramakić, Adnan, Diego Sušanj, Kristijan Lenac, and Zlatko Bundalo. "Depth-Based Real-Time Gait Recognition." Journal of Circuits, Systems and Computers 29, no. 16 (June 30, 2020): 2050266. http://dx.doi.org/10.1142/s0218126620502667.

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Each person describes unique patterns during gait cycles and this information can be extracted from live video stream and used for subject identification. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. In this paper, a method to enhance the appearance-based gait recognition method by also integrating features extracted from depth data is proposed. Two approaches are proposed that integrate simple depth features in a way suitable for real-time processing. Unlike previously presented works which usually use a short range sensors like Microsoft Kinect, here, a long-range stereo camera in outdoor environment is used. The experimental results for the proposed approaches show that recognition rates are improved when compared to existing popular gait recognition methods.

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16

Maiti, Somsukla, Sandeep Reddy, and Jagdish Lal Raheja. "View invariant real-time gesture recognition." Optik 126, no. 23 (December 2015): 3737–42. http://dx.doi.org/10.1016/j.ijleo.2015.08.243.

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17

El-Sheikh, T. S., and S. G. El-Taweel. "Real-time arabic handwritten character recognition." Pattern Recognition 23, no. 12 (January 1990): 1323–32. http://dx.doi.org/10.1016/0031-3203(90)90078-y.

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18

Saad, N. M., N. S. M. Noor, A. R. Abdullah, O. Y. Fong, and N. N. S. A. Rahman. "Real-Time LCD Digit Recognition System." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 2 (May 1, 2017): 402. http://dx.doi.org/10.11591/ijeecs.v6.i2.pp402-411.

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Анотація:

<p>In recent years, the utilization of digital instruments in industries is quickly expanding. This is because digital instruments are typically more exact than the analog instruments, and easier to be read as they are hooked up to a liquid-crystal display (LCD). However, manual data entry from LCD display is tedious and less accurate. This paper proposes a real-time LCD digit recognition system for the industrial purposes. The system is interfaced with an IP webcam to capture the video frames from the LCD display. The digital data is pre-processed into grayscale and being cropped into a selected region of interest (ROI). Adaptive thresholding and morphological operation are applied for the digit segmentation process. Data extraction and characterization are done by utilizing neural network classifier. Finally, all the information are logged out to Microsoft Excel spreadsheet. The 90% accuracy is accomplished for 50 test images of various LCD display.</p>

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19

Jagtap, Supriya, and K. R. Desai. "REAL-TIME SPEECH BASED SENTIMENT RECOGNITION." Far East Journal of Electronics and Communications 21, no. 1-2 (November 15, 2019): 43–52. http://dx.doi.org/10.17654/ec021120043.

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20

Nishanth, R., Abin John Joseph, and Naveen S. "Design of a Real Time Modular Brain-Computer Interface movement-related EEG recognition." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1884–88. http://dx.doi.org/10.31142/ijtsrd11414.

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21

Yan Ran, 鄢然, 张李超 Zhang Lichao, 张宜生 Zhang Yisheng, and 赵祖烨 Zhao Zuye. "Tricot Lace Real-Time Recognition Method Based on Feature Recognition." Laser & Optoelectronics Progress 52, no. 11 (2015): 111002. http://dx.doi.org/10.3788/lop52.111002.

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22

Malipatil, Sridevi. "Real Time Sign Language RecognitionReal Time Sign Language Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2032–36. http://dx.doi.org/10.22214/ijraset.2022.44266.

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Abstract: With regard to hearing and vocally impaired individualities, communication with others is a way longer struggle for them. They are unfit to speak with traditional individualities duly. They face difficulties in getting jobs and living a traditional life like others. In this paper, we are introducing a smart communication system for hearing and vocally impaired individuals and also for normal people. The overall delicacy of the system is 92.5, with both the hands involved. The main advantage of this system being proposed over the former system is that in the former system the signs can be detectedby the camera only when the hands are covered in gloves whereas in this proposed system, we have tried our swish to overcome that disadvantage handed by the former system. Keywords: Open CV, Google API, Raspberry Pi video core GPU, image pre-processing, feature extraction.

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23

Omar, Herman Khalid, Shahad Fauzi Mohammed, and Rana Adib Khisro. "Real-Time Object Recognition Using Deep-Learning." Academic Journal of Nawroz University 10, no. 2 (May 8, 2021): 47–53. http://dx.doi.org/10.25007/ajnu.v10n2a1073.

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24

Et. al., Manisha A,. "TAMIL SIGN LANGUAGE RECOGNITION IN REAL TIME." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (April 13, 2021): 1112–16. http://dx.doi.org/10.17762/itii.v9i2.459.

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Анотація:

Communication is interchanging of ideas information or message from one person to the other person, sign language is made with the hand and other movement, including facial expressions and posture of the body used by the people of unable to speak and hearing, there are different type of sign language. Tamil sign language is the regional sign language, the aim of the work is provide the real time recognition of Tamil sign Language (TSL) in to Tamil letter, here we introduced the convolutional neural network (CNN) as a classifier used as training the Tamil sign language and predict the Tamil sign language, the process are divided in two section one is to train by using the keras model and the second section is the skin segmentation of the hand gestures in the region of the interest, there are two phase in training the model, having set of the hand gestures images in the training set and testing set by using the image train the model, the model occurred the 100% accuracy in the white background and good lighting condition, 97.5 % in the low lighting condition.

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25

Fadhil, Ahmed, Mayada Faris, Ali Al-Saegh, and Mohammad Mohammad. "Real-Time Signature Recognition Using Neural Network." Al-Rafidain Engineering Journal (AREJ) 26, no. 1 (January 1, 2021): 159–65. http://dx.doi.org/10.33899/rengj.2021.129871.1088.

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26

Alkababji, Ahmed M., and Omar H. Mohammed. "Real time ear recognition using deep learning." TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no. 2 (April 1, 2021): 523. http://dx.doi.org/10.12928/telkomnika.v19i2.18322.

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27

Dass, Sanchit, Mohammed Sadrulhuda, Navaz Pasha, Nishant Nayan, and Jyothi S. "Real Time Face Recognition using Raspberry Pi." International Journal of Computer Applications 176, no. 33 (June 18, 2020): 1–4. http://dx.doi.org/10.5120/ijca2020920387.

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28

Bulygin, Dmitriy, and Tatyana Mamonova. "Recognition of hand gestures in real time." Science Bulletin of the Novosibirsk State Technical University, no. 1 (March 20, 2020): 25–40. http://dx.doi.org/10.17212/1814-1196-2020-1-25-40.

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29

Sarno, Riyanarto, Muhammad Nadzeri Munawar, and Brilian T. Nugraha. "Real-Time Electroencephalography-Based Emotion Recognition System." International Review on Computers and Software (IRECOS) 11, no. 5 (May 31, 2016): 456. http://dx.doi.org/10.15866/irecos.v11i5.9334.

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30

Khan, Imran Ahmed, Mukesh Maurya, Rishabh Gupta, and Prince Singh. "Real Time Barcode Recognition System using LabVIEW." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 4 (April 30, 2017): 68–72. http://dx.doi.org/10.23956/ijarcsse/v7i4/0141.

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31

Ansari, Fatima, Anwar Hussain Mistry, Yusuf Mirkar, and Alim Merchant. "Real Time ASL (American Sign Language) Recognition." International Journal of Computer Sciences and Engineering 7, no. 2 (February 28, 2019): 848–51. http://dx.doi.org/10.26438/ijcse/v7i2.848851.

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32

Guo, Jiang, Jun Cheng, Yu Guo, and Jian Xin Pang. "A Real-Time Dynamic Gesture Recognition System." Applied Mechanics and Materials 333-335 (July 2013): 849–55. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.849.

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Анотація:

In this paper, we present a dynamic gesture recognition system. We focus on the visual sensory information to recognize human activity in form of hand movements from a small, predefined vocabulary. A fast and effective method is presented for hand detection and tracking at first for the trajectory extraction. A novel trajectory correction method is applied for simply but effectively trajectory correction. Gesture recognition is achieved by means of a matching technique by determining the distance between the unknown input direction code sequence and a set of previously defined templates. A dynamic time warping (DTW) algorithm is used to perform the time alignment and normalization by computing a temporal transformation allowing the two signals to be matched. Experiment results show our proposed gesture recognition system achieve well result in real time.

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33

Azad, Babak, and Eslam Ahmadzade. "Real-Time Multiple License Plate Recognition System." International Journal of Research in Computer Science 4, no. 2 (March 5, 2014): 11–17. http://dx.doi.org/10.7815/ijorcs.42.2014.080.

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34

Sasikumar, Dr R., Dr P. Shanmugaraja, K. Kailash, M. Prudhvi Charan Reddy, and S. Nikhil Jagadeesh. "Real-time Facemask Recognition Using Deep Learning." Revista Gestão Inovação e Tecnologias 11, no. 2 (June 8, 2021): 2079–85. http://dx.doi.org/10.47059/revistageintec.v11i2.1828.

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Анотація:

The investigation of PC vision is especially fascinating because of the worldwide plague COVID-19 to improve general well being administrations. At the hour of death, the disclosure of a little item is a difficult assignment of taking a gander at a PC, as it catches the division and revelation under the video picture. Contrasted with other item disclosure profound neural organizations have shown the revelation of supported articles by acquiring a higher veil. Nonetheless, admittance to the proposed framework is covered by an uncommon point that absolutely occurs with regular infections individuals profit by. Added to completing a face cover well, which estimates constant execution corresponding to an incredible GPU. Test outcomes showing a regular misfortune are 0.0730 in the wake of preparing 4000 ages. Subsequent to preparing 4000 focuses, ages mAP 0.96. This exceptional face concealing framework gets visual yield with 96% separation and identification affectability.

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35

Sharma, Sarthak. "Real-Time Sign Language Detection and Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1944–48. http://dx.doi.org/10.22214/ijraset.2021.39103.

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Abstract: Sign language is one of the oldest and most natural form of language for communication, but since most people do not know sign language and interpreters are very difficult to come by we have come up with a real time method using neural networks for fingerspelling based American sign language. In our method, the hand is first passed through a filter and after the filter is applied the hand is passed through a classifier which predicts the class of the hand gestures.

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36

Mulla, Vasim Bashir. "Real Time Access Control using Face Recognition." International Journal for Research in Applied Science and Engineering Technology 7, no. 3 (March 31, 2019): 1148–50. http://dx.doi.org/10.22214/ijraset.2019.3204.

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37

Minami, Mamoru, Julien Agbanhan, Hidekazu Suzuki, and Toshiyuki Asakura. "Real-time Corridor Recognition for Autonomous Vehicle." Journal of Robotics and Mechatronics 13, no. 4 (August 20, 2001): 357–70. http://dx.doi.org/10.20965/jrm.2001.p0357.

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Recognition of a working environment is critical for an autonomous vehicle such as a mobile robot to guide it along corridor and to confirm its possible intelligence. Therefore it is necessary to equip a recognition system with sensor that collect environmental information. As an effective sensor a CCD camera is generally useful for all kinds of mobile robots. However, it is hard to use the CCD camera for visual feedback since it requires to acquire information in real-time, and moreover to be robust against lighting condition varieties. This research presents a corridor recognition method using unprocessed gray-scale image, termed a raw image, and a genetic algorithm (GA), without any image information conversion, to conduct the recognition process in real-time. To achieve robustness concerning lighting condition varieties, we propose a model-matching method using a representative object model designated here as surface-strips model. The robustness of the method against noise in the environment, including lighting conditions variations, and the effectiveness of the method for real-time recognition have been verified using real corridor images.

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38

S, Simran. "Real-Time Emotion Recognition through Facial Expressions." International Journal for Research in Applied Science and Engineering Technology 7, no. 5 (May 31, 2019): 72–76. http://dx.doi.org/10.22214/ijraset.2019.5013.

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39

Kwon, Man-Jun, Dong-Hwa Yang, Hyoun-Joo Go, Jin-Whan Kim, and Myung-Geun Chun. "Real-Time Face Recognition System using PDA." Journal of Korean Institute of Intelligent Systems 15, no. 5 (October 1, 2005): 649–54. http://dx.doi.org/10.5391/jkiis.2005.15.5.649.

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40

Li, Hsin-Yu Sidney, Yong Qiao, and Demetri Psaltis. "Optical network for real-time face recognition." Applied Optics 32, no. 26 (September 10, 1993): 5026. http://dx.doi.org/10.1364/ao.32.005026.

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41

Liu, Yifeng. "Haar-SVM for Real-time Banknotes Recognition." Journal of Information and Computational Science 11, no. 12 (August 10, 2014): 4031–39. http://dx.doi.org/10.12733/jics20104321.

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42

Ayu Wirdiani, Ni Kadek, Tita Lattifia, I. Kadek Supadma, Boy Jehezekiel Kemanang Mahar, Dewa Ayu Nadia Taradhita, and Adi Fahmi. "Real-Time Face Recognition with Eigenface Method." International Journal of Image, Graphics and Signal Processing 11, no. 11 (November 8, 2019): 1–9. http://dx.doi.org/10.5815/ijigsp.2019.11.01.

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43

Lee, Kwang-Ryeol, Won-Du Chang, Sungkean Kim, and Chang-Hwan Im. "Real-Time “Eye-Writing” Recognition Using Electrooculogram." IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, no. 1 (January 2017): 37–48. http://dx.doi.org/10.1109/tnsre.2016.2542524.

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44

Hui, Qiu, and Wang Kun. "Real-time Network Attack Intention Recognition Algorithm." International Journal of Security and Its Applications 10, no. 4 (April 30, 2016): 51–62. http://dx.doi.org/10.14257/ijsia.2016.10.4.06.

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45

Kruchinin, A. Yu. "Multitasking system for real time pattern recognition." Automation and Remote Control 76, no. 1 (January 2015): 166–71. http://dx.doi.org/10.1134/s0005117915010166.

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46

Bao Zhiqiang, 鲍志强, and 吕辰刚 Lü Chengang. "Real-Time Gesture Recognition Based on Kinect." Laser & Optoelectronics Progress 55, no. 3 (2018): 031008. http://dx.doi.org/10.3788/lop55.031008.

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