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Статті в журналах з теми "RECOGNITION APPLICATIONS"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

Begalinova, A., and A. Shintemirov. "EMBEDDED GESTURE RECOGNITION SYSTEM FOR ROBOTIC APPLICATIONS." Eurasian Journal of Mathematical and Computer Applications 2, no. 1 (2014): 81–89. http://dx.doi.org/10.32523/2306-3172-2014-2-4-81-89.

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2

Hamzh, Al Rubaie Evan Madhi. "Text Recognition Applications." IJARCCE 5, no. 10 (October 30, 2016): 603–7. http://dx.doi.org/10.17148/ijarcce.2016.510122.

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3

Templeton, Douglas, and Michael Schwenk. "Immunochemical Recognition and Applications." Pure and Applied Chemistry 86, no. 10 (October 21, 2014): 1433–34. http://dx.doi.org/10.1515/pac-2014-5053.

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4

Singh, Nilu, R. A. Khan, and Raj Shree. "Applications of Speaker Recognition." Procedia Engineering 38 (2012): 3122–26. http://dx.doi.org/10.1016/j.proeng.2012.06.363.

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5

L. Almeida, Leandro, Maria S.V. Paiva, Francisco A. Silva, and Almir O. Artero. "Super-Resolution Images Enhanced for Applications to Character Recognition." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 01, no. 03 (August 16, 2013): 09–16. http://dx.doi.org/10.9756/sijcsea/v1i3/0103520101.

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6

Busschaert, Nathalie, Claudia Caltagirone, Wim Van Rossom, and Philip A. Gale. "Applications of Supramolecular Anion Recognition." Chemical Reviews 115, no. 15 (May 21, 2015): 8038–155. http://dx.doi.org/10.1021/acs.chemrev.5b00099.

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7

Schlenoff, Craig, Zeid Kootbally, Anthony Pietromartire, Marek Franaszek, and Sebti Foufou. "Intention recognition in manufacturing applications." Robotics and Computer-Integrated Manufacturing 33 (June 2015): 29–41. http://dx.doi.org/10.1016/j.rcim.2014.06.007.

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8

Rabiner, Lawrence R. "Speech recognition: Technology and applications." Journal of the Acoustical Society of America 88, S1 (November 1990): S197. http://dx.doi.org/10.1121/1.2028883.

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9

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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Анотація:

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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10

Arnav Madan. "Face Recognition using Haar Cascade Classifier." January 2021 7, no. 01 (January 29, 2021): 85–87. http://dx.doi.org/10.46501/ijmtst070119.

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With development of machine learning technology many applications have been revolutionized which earlier usedto utilize high amoun to fresources. Face recognition is a crucial security application. Though this paper we present this application using optimized amount of resources and high efficiency.

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11

GUAN, Xin. "Attribute measure recognition approach and its applications to emitter recognition." Science in China Series F 48, no. 2 (2005): 225. http://dx.doi.org/10.1360/122004-82.

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12

Kumar, Bhavesh Shri, Naren J, Prahathish K, and Vithya G. "Enumeration on the various tenets in Scene Recognition – Applications and Techniques." International Journal of Psychosocial Rehabilitation 23, no. 1 (February 20, 2019): 358–65. http://dx.doi.org/10.37200/ijpr/v23i1/pr190245.

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13

M C, Sohan, Akanksh A M, Anala M R, and Hemavathy R. "Banknote Denomination Recognition on Mobile Devices." ECS Transactions 107, no. 1 (April 24, 2022): 11781–90. http://dx.doi.org/10.1149/10701.11781ecst.

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Анотація:

Several mobile applications have been developed to facilitate denomination detection for blind users. However, none of the existing applications allow for detecting multiple notes in a single frame and relaying the total denomination, nor is there a dataset available for the new Indian currency notes, annotated for object detection training. We describe the development of a detection application that aims to improve on the previously existing solutions by enabling multi-note detection, continuous audio feedback, automatic torch usage, and minimal user-application interaction. YOLOv4 allowed the training of a lightweight and fast object detection model with high accuracy on a custom-created dataset post-demonetization of Indian currencies that is deployed on a mobile device.

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14

Dey, Sreya. "A Facial Recognition System." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 3863–64. http://dx.doi.org/10.22214/ijraset.2022.45904.

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Abstract: This paper introduces the design, implementation, and validation of a Digital Signal Processor (DSP) -based Prototype face recognition and authentication system. This system is designed to capture image sequences, detect facial features in photos, and detect and verify a person. The current application uses images captured on a webcam and compares them to archived websites using the Comprehensive Component Analysis (PCA) and Discrete Cosine Transform (DCT) methods. Initially, realtime verification of the captured images was performed using a PC-based program with algorithms developed in MATLAB. Next, the TMS320C6713DSP-based prototype system is upgraded and validated in real-time. Several tests are performed on different sets of images, and the performance and speed of the proposed system are measured in real-time. Finally, the result confirmed that the proposed system could be used in a variety of applications that are not possible in standard PC-based applications. Also, better results were seen from DCT analysis than PCA results.

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15

Bilyk, Zhanna I., Yevhenii B. Shapovalov, Viktor B. Shapovalov, Anna P. Megalinska, Sergey O. Zhadan, Fabian Andruszkiewicz, Agnieszka Dołhańczuk-Śródka, and Pavlo D. Antonenko. "Comparison of Google Lens recognition performance with other plant recognition systems." Educational Technology Quarterly 2022, no. 4 (December 21, 2022): 328–46. http://dx.doi.org/10.55056/etq.433.

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Анотація:

In the context of the STEM approach to education, motivating pupils through tailored research and leveraging IT in the classroom is relevant. The justification of these approaches hasn't received much examination, though. The purpose of the study is to support the decision to use an AR-plant recognition application to give tailored instruction throughout both extracurricular activities and the school day. Every phase of an app's interaction with a user was examined and used to categorize every app. Also described were the social settings of the applications and how they were used for extracurricular activities. There has been discussion on the didactics of using AR recognition apps in biology classes. A survey of experts in digital education regarding the ease of installation, the friendliness of the interface, and the accuracy of image processing was conducted to give usability analysis. Applications were examined for their ability to accurately identify plants on the "Dneprovskiy district of Kiev" list in order to assess the rationale of usage. It has been established that Google Lens is the best option. As an alternative to Seek or Flora Incognita, according to the analysis's findings, these apps were less accurate.

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16

Li, Vladislav, Georgios Amponis, Jean-Christophe Nebel, Vasileios Argyriou, Thomas Lagkas, and Panagiotis Sarigiannidis. "OBJECT RECOGNITION FOR AUGMENTED REALITY APPLICATIONS." Azerbaijan Journal of High Performance Computing 4, no. 1 (June 30, 2021): 15–28. http://dx.doi.org/10.32010/26166127.2021.4.1.15.28.

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Developments in the field of neural networks, deep learning, and increases in computing systems’ capacity have allowed for a significant performance boost in scene semantic information extraction algorithms and their respective mechanisms. The work presented in this paper investigates the performance of various object classification- recognition frameworks and proposes a novel framework, which incorporates Super-Resolution as a preprocessing method, along with YOLO/Retina as the deep neural network component. The resulting scene analysis framework was fine-tuned and benchmarked using the COCO dataset, with the results being encouraging. The presented framework can potentially be utilized, not only in still image recognition scenarios but also in video processing.

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17

Ballester, Pedro J. "Ultrafast shape recognition: method and applications." Future Medicinal Chemistry 3, no. 1 (January 2011): 65–78. http://dx.doi.org/10.4155/fmc.10.280.

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18

Kober, Vitaly, Tae Choi, Victor Diaz-Ramírez, and Pablo Aguilar-González. "Pattern Recognition: Recent Advances and Applications." Mathematical Problems in Engineering 2018 (November 15, 2018): 1–2. http://dx.doi.org/10.1155/2018/8510319.

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19

Ren, Dong, and Simon X. Yang. "Intelligent Pattern Recognition Technology and Applications." Intelligent Automation & Soft Computing 19, no. 4 (December 2013): 497–99. http://dx.doi.org/10.1080/10798587.2013.869107.

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20

Bannach, David, Oliver Amft, and Paul Lukowicz. "Rapid Prototyping of Activity Recognition Applications." IEEE Pervasive Computing 7, no. 2 (April 2008): 22–31. http://dx.doi.org/10.1109/mprv.2008.36.

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21

Fu, Haibin, Huaping Liu, Xiaoyan Deng, and Fuchun Sun. "Wood material recognition for industrial applications." Systems Science & Control Engineering 6, no. 3 (September 21, 2018): 346–58. http://dx.doi.org/10.1080/21642583.2018.1553691.

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22

Paolanti, Marina, and Emanuele Frontoni. "Multidisciplinary Pattern Recognition applications: A review." Computer Science Review 37 (August 2020): 100276. http://dx.doi.org/10.1016/j.cosrev.2020.100276.

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23

Shirai, Katsuhiko. "3. Speech Recognition Techniques and Applications." Journal of the Institute of Television Engineers of Japan 41, no. 8 (1987): 716–25. http://dx.doi.org/10.3169/itej1978.41.716.

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24

Tran, Dat, Wanli Ma, and Dharmendra Sharma. "Handwriting Recognition Applications for Tablet PCs." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 7 (September 20, 2007): 787–92. http://dx.doi.org/10.20965/jaciii.2007.p0787.

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This paper presents handwriting recognition applications developed and tested on the tablet PC – a new generation of notebook computers. Users write on a tablet PC screen with a tablet pen and a built-in user-independent handwriting recognition tool converts handwritings to printed text. We present handwriting recognition applications using the built-in recognition tool and signature verification using our own verification tool based on fuzzy c-means vector quantization (FCMVQ) and observable Markov modeling (OMM). Experimental results for the signature verification system are also presented.

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25

Moreno-Bondi, María Cruz. "Biomimetic recognition elements for sensing applications." Analytical and Bioanalytical Chemistry 402, no. 10 (February 11, 2012): 3019–20. http://dx.doi.org/10.1007/s00216-012-5778-0.

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26

Lavine, Barry K. "Environmental applications of pattern recognition techniques." Chemometrics and Intelligent Laboratory Systems 15, no. 2-3 (August 1992): 219–30. http://dx.doi.org/10.1016/0169-7439(92)85011-q.

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27

Moreno-Bondi, María Cruz, and Elena Benito-Peña. "Analytical applications of biomimetic recognition elements." Analytical and Bioanalytical Chemistry 408, no. 7 (December 23, 2015): 1725–26. http://dx.doi.org/10.1007/s00216-015-9220-2.

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28

Khachumov, M. V., V. M. Khachumov, A. K. Kovalev, and A. I. Panov. "Pattern-Recognition Tools and Their Applications." Pattern Recognition and Image Analysis 33, no. 1 (March 2023): 28–38. http://dx.doi.org/10.1134/s1054661823010029.

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29

LEMAN, K., G. ANKIT, and T. TAN. "PDA BASED HUMAN MOTION RECOGNITION SYSTEM." International Journal of Software Engineering and Knowledge Engineering 15, no. 02 (April 2005): 199–204. http://dx.doi.org/10.1142/s021819400500218x.

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Анотація:

This paper describes the design and implementation of autonomous real-time motion recognition on a Personal Digital Assistant. All previous such applications have been non real-time and required user interaction. The motivation to use a PDA is to test the viability of performing complex video processing on an embedded platform. The application was constructed using a representation and recognition technique for identifying patterns using Hu Moments. The approach is based upon temporal templates (Motion Energy and History Images) and their matching in time. The implementation was done using Intel Integrated Performance Primitives functions in order to reduce the complexity of the application. Tests were conducted using 5 different motion actions like arm waving, walking from left and right of the camera, head tilting and bending forward. Suggestions were also made on how to improve the performance of the system and possible applications.

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30

RAUTARAY, SIDDHARTH SWARUP, and ANUPAM AGRAWAL. "HAND GESTURE RECOGNITION TOWARDS VOCABULARY AND APPLICATION INDEPENDENCY." International Journal of Image and Graphics 13, no. 02 (April 2013): 1340001. http://dx.doi.org/10.1142/s0219467813400019.

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Анотація:

Traditional human–computer interaction devices such as the keyboard and mouse become ineffective for an effective interaction with the virtual environment applications because the 3D applications need a new interaction device. An efficient human interaction with the modern virtual environments requires more natural devices. Among them the "Hand Gesture" human–computer interaction modality has recently become of major interest. The main objective of gesture recognition research is to build a system which can recognize human gestures and utilize them to control an application. One of the drawbacks of present gesture recognition systems is being application-dependent which makes it difficult to transfer one gesture control interface into multiple applications. This paper focuses on designing a hand gesture recognition system which is vocabulary independent as well as adaptable to multiple applications. This makes the proposed system vocabulary independent and application independent. The designed system is comprised of the different processing steps like detection, segmentation, tracking, recognition, etc. Vocabulary independence has been incorporated in the proposed system with the help of a robust gesture mapping module that allows the user for cognitive mapping of different gestures to the same command and vice versa. For performance analysis of the proposed system accuracy, recognition rate and command response time have been compared. These parameters have been considered because they analyze the vital impact on the performance of the proposed vocabulary and application-independent hand gesture recognition system.

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31

Frenza, Devira, and Riki Mukhaiyar. "Aplikasi Pengenalan Wajah dengan Metode Adaptive Resonance Theory (ART)." Ranah Research : Journal of Multidisciplinary Research and Development 3, no. 3 (May 12, 2021): 147–53. http://dx.doi.org/10.38035/rrj.v3i3.392.

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Анотація:

Image processing technology (image processing) at this time can be done using a computer. An image that can be recognized by a computer is called a digital image. One application of image processing (image processing) is the field of face detection (face detection). Today's face detection system is one of the most important technologies. Because, facial recognition systems are closely related to security systems, where facial recognition systems can be applied in various places and situations, namely: security system applications, facial disguise recognition applications, and database information system applications for an institution. In this study, we will discuss facial recognition using the Principal Component Analysis (PCA) method and the Artificial Neural Network (ANN) used is Adaptive Resonance Theory (ART). The application used to conduct this research is Matlab.

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32

Everhart, Charles A. "Advanced voice recognition phone interface for in-vehicle speech recognition applications." Journal of the Acoustical Society of America 118, no. 1 (2005): 29. http://dx.doi.org/10.1121/1.1999433.

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33

Et. al., Anupam,. "Recognition of Distinctive Reliability Leading Factors for Mobile Applications." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 4 (April 10, 2021): 488–93. http://dx.doi.org/10.17762/turcomat.v12i4.530.

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Анотація:

Software reliability is a significant quality characteristic, and reliability models are often used to gauge and anticipate programming development. The quality of versatile apps conditions contrasts from that of PC and server conditions because of numerous elements, like the organization, energy, battery, and similarity. Assessing and anticipating versatile application dependability are genuine difficulties in light of the variety of the portable conditions in which the applications are utilized, and the absence of openly accessible deformity information. Also, bug reports are alternatively put together by end-clients. In the current research work, in view of the writing survey and specialist’s assessment working in the field of versatile application advancement, 10 reliability leading factors and 14 sub-factors have been recognized that are fundamental for evaluating reliability of a portable applications.

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34

Gayatri, Keerthi, MuttumVenkata Yamini, Ukkadapu Thanmayee, Medikonda Bhagya Jyothi, M. Srinivasa Rao, and D. Janardhan Reddy. "Age and Gender Identification Using Neural Networks." International Journal of Innovative Research in Engineering and Management 9, no. 2 (April 26, 2022): 644–47. http://dx.doi.org/10.55524/ijirem.2022.9.2.102.

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Face recognition is still very challenging and complex problem. This problem can be credited as large intra-personal variations and large inter personal similarity. Facial recognition is the application of biometric breakthroughs that can observe or verify a person by observing and examining designs based on the individual's shape. Despite the increased interest in other applications, face recognition is still mostly utilized for well-being. Generally, advancements in face recognition are worthwhile since they may have a wide range of legal applications and commercial uses.

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35

MAIYA, BHASKAR G. "Molecular recognition." Journal of Porphyrins and Phthalocyanines 04, no. 04 (June 2000): 393–97. http://dx.doi.org/10.1002/(sici)1099-1409(200006/07)4:4<393::aid-jpp227>3.0.co;2-b.

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Анотація:

The ‘marriage’ between porphyrin chemistry and the newly emerging molecular recognition science has opened up novel research opportunities leading to many biological and abiological applications. An overview of this fascinating research area is presented here.

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36

Rabko, Michael, and Martin Ebner. "1x1 Trainer with Handwriting Recognition." International Journal of Interactive Mobile Technologies (iJIM) 12, no. 2 (March 29, 2018): 69. http://dx.doi.org/10.3991/ijim.v12i2.7714.

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<p class="0abstract">Nowadays, computers and mobile devices play a huge role in our daily routines; they are used at work, for private purposes and even at school. Moreover, they are used as support for different kinds of activities and task, like for example, learning applications. The interaction of these applications with a computer is based on predefined input methods, whereas a touchscreen facilitates direct input via handwriting by using a finger or a pen.</p>This paper deals with the invention of a mobile learning application, which is supposed to facilitate children’s learning of simple multiplication. The aim of this paper is to collect the data of children’ experiences using interactive handwriting on mobile devices. In order to gain this data, a school class of the school “Graz-Hirten” was tested and afterwards for evaluational purposes interviewed. The results of these usability tests have shown that children perceived handwriting via finger on screen as quite positive.

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37

Doroshenko, Anna. "Applying Artificial Neural Networks In Construction." E3S Web of Conferences 143 (2020): 01029. http://dx.doi.org/10.1051/e3sconf/202014301029.

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Currently, artificial neural networks (ANN) are used to solve the following complex problems: pattern recognition, speech recognition, complex forecasts and others. The main applications of ANN are decision making, pattern recognition, optimization, forecasting, data analysis. This paper presents an overview of applications of ANN in construction industry, including energy efficiency and energy consumption, structural analysis, construction materials, smart city and BIM technologies, structural design and optimization, application forecasting, construction engineering and soil mechanics.

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38

Wu, Jin, Yaqiao Zhu, Chunguang Wang, Jinfu Li, and Xuehong Zhu. "A Prior Knowledge-Guided Graph Convolutional Neural Network for Human Action Recognition in Solar Panel Installation Process." Applied Sciences 13, no. 15 (July 26, 2023): 8608. http://dx.doi.org/10.3390/app13158608.

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Анотація:

Human action recognition algorithms have garnered significant research interest due to their vast potential for applications. Existing human behavior recognition algorithms primarily focus on recognizing general behaviors using a large number of datasets. However, in industrial applications, there are typically constraints such as limited sample sizes and high accuracy requirements, necessitating algorithmic improvements. This article proposes a graph convolution neural network model that combines prior knowledge supervision and attention mechanisms, designed to fulfill the specific action recognition requirements for workers installing solar panels. The model extracts prior knowledge from training data, improving the training effectiveness of action recognition models and enhancing the recognition reliability of special actions. The experimental results demonstrate that the method proposed in this paper surpasses traditional models in terms of recognizing solar panel installation actions accurately. The proposed method satisfies the need for highly accurate recognition of designated person behavior in industrial applications, showing promising application prospects.

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39

Lu, Shan. "The Research on the Face Recognition Technology." Applied Mechanics and Materials 263-266 (December 2012): 2651–54. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2651.

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Анотація:

This article mainly introduces the face recognition technology in its development，the present situation and common applications, and also discusses the facing recognition problems and solutions,finally it makes a brief outlook for the future of face recognition technology in development and application.

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40

Galaverna, G., C. Dall'Asta, R. Corradini, A. Dossena, and R. Marchelli. "Cyclodextrins as selectors for mycotoxin recognition." World Mycotoxin Journal 1, no. 4 (November 1, 2008): 397–406. http://dx.doi.org/10.3920/wmj2008.1022.

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Анотація:

This review deals with the applications of cyclodextrins as selectors for mycotoxin recognition. Complexation by cyclodextrins via formation of inclusion (host-guest) complexes induces significant changes in the physical and chemical properties of mycotoxins as guest molecules, effects that can be used in a variety of analytical techniques. Changes in chromatographic and electrophoretic properties and their applications to set up new separation methods are covered. Among these changes, a significant effect is the enhancement of the mycotoxin fluorescence upon inclusion, a phenomenon which provides a simple and convenient method to significantly increase the sensitivity of fluorescence-based trace analysis. The practical application of this phenomenon to set up new analytical methods is described. Studies on the mechanism of inclusion complex formation are also reported.

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41

Zaqout, Ihab, and Mones Al-Hanjori. "An improved technique for face recognition applications." Information and Learning Science 119, no. 9/10 (October 8, 2018): 529–44. http://dx.doi.org/10.1108/ils-03-2018-0023.

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Purpose The face recognition problem has a long history and a significant practical perspective and one of the practical applications of the theory of pattern recognition, to automatically localize the face in the image and, if necessary, identify the person in the face. Interests in the procedures underlying the process of localization and individual’s recognition are quite significant in connection with the variety of their practical application in such areas as security systems, verification, forensic expertise, teleconferences, computer games, etc. This paper aims to recognize facial images efficiently. An averaged-feature based technique is proposed to reduce the dimensions of the multi-expression facial features. The classifier model is generated using a supervised learning algorithm called a back-propagation neural network (BPNN), implemented on a MatLab R2017. The recognition rate and accuracy of the proposed methodology is comparable with other methods such as the principle component analysis and linear discriminant analysis with the same data set. In total, 150 faces subjects are selected from the Olivetti Research Laboratory (ORL) data set, resulting 95.6 and 85 per cent recognition rate and accuracy, respectively, and 165 faces subjects from the Yale data set, resulting 95.5 and 84.4 per cent recognition rate and accuracy, respectively. Design/methodology/approach Averaged-feature based approach (dimension reduction) and BPNN (generate supervised classifier). Findings The recognition rate is 95.6 per cent and recognition accuracy is 85 per cent for the ORL data set, whereas the recognition rate is 95.5 per cent and recognition accuracy is 84.4 per cent for the Yale data set. Originality/value Averaged-feature based method.

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42

Yang, Xinyan, Yunhui Yi, Xinguang Xiao, and Yanhong Meng. "Mobile Application Identification based on Hidden Markov Model." ITM Web of Conferences 17 (2018): 02002. http://dx.doi.org/10.1051/itmconf/20181702002.

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With the increasing number of mobile applications, there has more challenging network management tasks to resolve. Users also face security issues of the mobile Internet application when enjoying the mobile network resources. Identifying applications that correspond to network traffic can help network operators effectively perform network management. The existing mobile application recognition technology presents new challenges in extensibility and applications with encryption protocols. For the existing mobile application recognition technology, there are two problems, they can not recognize the application which using the encryption protocol and their scalability is poor. In this paper, a mobile application identification method based on Hidden Markov Model(HMM) is proposed to extract the defined statistical characteristics from different network flows generated when each application starting. According to the time information of different network flows to get the corresponding time series, and then for each application to be identified separately to establish the corresponding HMM model. Then, we use 10 common applications to test the method proposed in this paper. The test results show that the mobile application recognition method proposed in this paper has a high accuracy and good generalization ability.

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43

a, Vinayak, and Rachana R. Babu. "Facial Emotion Recognition." YMER Digital 21, no. 05 (May 23, 2022): 1010–15. http://dx.doi.org/10.37896/ymer21.05/b5.

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Human expresses their mood and sometimes what they need through their expression. This project traces the mood of the human using a real time recognition system which will detect the emotion. It can be a smiling face, or it can be the face full of anger. Facial emotion recognition is one of the useful task and can be used as a base for many real-time applications. The example can be feedback through moods at any restaurants and hotels about their services and foods. It can be much impactful in the field of military. Its very usage can be helpful for recognizing the people’s behaviour at the border areas to find out the suspects between them. This project consists of various algorithms of machine as well as deep learning. Some of the libraries are: Keras, OpenCV, Matplotlib. Image processing is used in classifying the universal emotions like neutral, surprise, sad, angry, happy, disguist, fear. This project consists of two modules: (i)Processing and generating the model for the application using different algorithms and (ii) Application for using the model using OpenCV to recognize. A set of values obtained after processing those extracted features points are given as input to recognize the emotion. Keywords: facial emotion recognition, deep neural networks, automatic recognition database

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44

RAUTARAY, SIDDHARTH S., and ANUPAM AGRAWAL. "VISION-BASED APPLICATION-ADAPTIVE HAND GESTURE RECOGNITION SYSTEM." International Journal of Information Acquisition 09, no. 01 (March 2013): 1350007. http://dx.doi.org/10.1142/s0219878913500071.

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With the increasing role of computing devices, facilitating natural human computer interaction (HCI) will have a positive impact on their usage and acceptance as a whole. For long time, research on HCI has been restricted to techniques based on the use of keyboard, mouse, etc. Recently, this paradigm has changed. Techniques such as vision, sound, speech recognition allow for much richer form of interaction between the user and machine. The emphasis is to provide a natural form of interface for interaction. Gestures are one of the natural forms of interaction between humans. As gesture commands are found to be natural for humans, the development of gesture control systems for controlling devices have become a popular research topic in recent years. Researchers have proposed different gesture recognition systems which act as an interface for controlling the applications. One of the drawbacks of present gesture recognition systems is application dependence which makes it difficult to transfer one gesture control interface into different applications. This paper focuses on designing a vision-based hand gesture recognition system which is adaptive to different applications thus making the gesture recognition systems to be application adaptive. The designed system comprises different processing steps like detection, segmentation, tracking, recognition, etc. For making the system as application-adaptive, different quantitative and qualitative parameters have been taken into consideration. The quantitative parameters include gesture recognition rate, features extracted and root mean square error of the system while the qualitative parameters include intuitiveness, accuracy, stress/comfort, computational efficiency, user's tolerance, and real-time performance related to the proposed system. These parameters have a vital impact on the performance of the proposed application adaptive hand gesture recognition system.

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45

Lu, Shan. "The Research on the Face Recognition Technology." Advanced Materials Research 271-273 (July 2011): 197–200. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.197.

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This article mainly introduces the face recognition technology in its development, the present situation and common applications, and also discusses the facing problems and solutions, finally it makes a brief outlook for the future of face recognition technology in development and application.

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46

Jiao, Chenlei, Binbin Lian, Zhe Wang, Yimin Song, and Tao Sun. "Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm." International Journal of Advanced Robotic Systems 17, no. 5 (September 1, 2020): 172988142094872. http://dx.doi.org/10.1177/1729881420948727.

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Object recognition is a prerequisite to control a soft gripper successfully grasping an unknown object. Visual and tactile recognitions are two commonly used methods in a grasping system. Visual recognition is limited if the size and weight of the objects are involved, whereas the efficiency of tactile recognition is a problem. A visual–tactile recognition method is proposed to overcome the disadvantages of both methods in this article. The design and fabrication of the soft gripper considering the visual and tactile sensors are implemented, where the Kinect v2 is adopted for visual information, bending and pressure sensors are embedded to the soft fingers for tactile information. The proposed method is divided into three steps: initial recognition by vision, detail recognition by touch, and a data fusion decision making. Experiments show that the visual–tactile recognition has the best results. The average recognition accuracy of the daily objects by the proposed method is also the highest. The feasibility of the visual–tactile recognition is verified.

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47

Ramakic, Adnan, Zlatko Bundalo, and Zeljko Vidovic. "Feature extraction for person gait recognition applications." Facta universitatis - series: Electronics and Energetics 34, no. 4 (2021): 557–67. http://dx.doi.org/10.2298/fuee2104557r.

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In this paper we present some features that may be used in person gait recognition applications. Gait recognition is an interesting way of people identification. During a gait cycle, each person creates unique patterns that can be used for people identification. Also, gait recognition methods ordinarily do not need interaction with a person and that is the main advantage of these methods. Features used in a person gait recognition methods can be obtained with widely available RGB and RGB-D cameras. In this paper we present a two features which are suitable for use in gait recognition applications. Mentioned features are height of a person and step length of a person. They may be extracted and were extracted from depth images obtained from RGB-D camera. For experimental purposes, we used a custom dataset created in outdoor environment using a long-range stereo camera.

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48

GUYON, I. "APPLICATIONS OF NEURAL NETWORKS TO CHARACTER RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 01n02 (June 1991): 353–82. http://dx.doi.org/10.1142/s021800149100020x.

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Among the many applications that have been proposed for neural networks, character recognition has been one of the most successful. Compared to other methods used in pattern recognition, the advantage of neural networks is that they offer a lot of flexibility to the designer, i.e. expert knowledge can be introduced into the architecture to reduce the number of parameters determined by training by examples. In this paper, a general introduction to neural network architectures and learning algorithms commonly used for pattern recognition problems is given. The design of a neural network character recognizer for on-line recognition of handwritten characters is then described in detail.

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Espinosa-Aranda, Jose, Noelia Vallez, Jose Rico-Saavedra, Javier Parra-Patino, Gloria Bueno, Matteo Sorci, David Moloney, Dexmont Pena, and Oscar Deniz. "Smart Doll: Emotion Recognition Using Embedded Deep Learning." Symmetry 10, no. 9 (September 7, 2018): 387. http://dx.doi.org/10.3390/sym10090387.

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Computer vision and deep learning are clearly demonstrating a capability to create engaging cognitive applications and services. However, these applications have been mostly confined to powerful Graphic Processing Units (GPUs) or the cloud due to their demanding computational requirements. Cloud processing has obvious bandwidth, energy consumption and privacy issues. The Eyes of Things (EoT) is a powerful and versatile embedded computer vision platform which allows the user to develop artificial vision and deep learning applications that analyse images locally. In this article, we use the deep learning capabilities of an EoT device for a real-life facial informatics application: a doll capable of recognizing emotions, using deep learning techniques, and acting accordingly. The main impact and significance of the presented application is in showing that a toy can now do advanced processing locally, without the need of further computation in the cloud, thus reducing latency and removing most of the ethical issues involved. Finally, the performance of the convolutional neural network developed for that purpose is studied and a pilot was conducted on a panel of 12 children aged between four and ten years old to test the doll.

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Ciora, Radu Adrian, and Carmen Mihaela Simion. "Industrial Applications of Image Processing." ACTA Universitatis Cibiniensis 64, no. 1 (November 1, 2014): 17–21. http://dx.doi.org/10.2478/aucts-2014-0004.

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Abstract The recent advances in sensors quality and processing power provide us with excellent tools for designing more complex image processing and pattern recognition tasks. In this paper we review the existing applications of image processing and pattern recognition in industrial engineering. First we define the role of vision in an industrial. Then a dissemination of some image processing techniques, feature extraction, object recognition and industrial robotic guidance is presented. Moreover, examples of implementations of such techniques in industry are presented. Such implementations include automated visual inspection, process control, part identification, robots control. Finally, we present some conclusions regarding the investigated topics and directions for future investigation

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