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노트

위키데이터

• ID : Q1519524

말뭉치

1. In this research article, vision based hand gesture recognition (HGR) System has been proposed using machine learning.
2. Our HGR system addresses the complex background problem and also improves the robustness of hand gesture recognition.
3. A versatile embedded platform for EMG acquisition and gesture recognition.
4. Gesture recognition by instantaneous surface EMG images.
5. Pancholi, S. & Joshi, A. M. Electromyography-based hand gesture recognition system for upper limb amputees.
6. An EMG gesture recognition system with flexible high-density sensors and brain-inspired high-dimensional classifier.
7. Gesture recognition using machine-learning methods is valuable in the development of advanced cybernetics, robotics and healthcare systems, and typically relies on images or videos.
8. The function of a hand gesture recognition system is to identify the type of movement, from a given set of movements, and the instant when that movement is performed.
9. Gesture recognition systems have multiple applications including sign language translation, bionics, human-machine interaction, gamming, and virtual reality.
10. For this reason, hand gesture recognition is a problem where many researchers have focused their attention too.
11. In this context, in this paper, we present a systematic literature review for hand gesture recognition using ma-chine learning and infrared information.
12. In this work, we present a novel real-time method for hand gesture recognition.
13. As we know, the vision-based technology of hand gesture recognition is an important part of human-computer interaction (HCI).
14. It is greatly different from the traditional hardware based methods and can accomplish human-computer interaction through gesture recognition.
15. Gesture recognition determines the user intent through the recognition of the gesture or movement of the body or body parts.
16. focuses in the field include emotion recognition from face and hand gesture recognition.
17. Using the concept of gesture recognition, it is possible to point a finger at this point will move accordingly.
18. The term gesture recognition has been used to refer more narrowly to non-text-input handwriting symbols, such as inking on a graphics tablet, multi-touch gestures, and mouse gesture recognition.
19. A standard 2D camera can be used for gesture recognition where the resources/environment would not be convenient for other forms of image-based recognition.
20. It’s not that unrealistic anymore: hand tracking and gesture recognition technologies are penetrating multiple industries.
21. Keep in mind that hand tracking and gesture recognition are not the same things.
22. Gesture recognition can also provide better ergonomics for consumer devices.
23. Real-time hand gesture recognition for computer interactions is just the next step in technological evolution, and it’s ideally suited for today’s consumer landscape.
24. In this paper, we present an approach for hand gesture recognition by 3D Convolutional Neural Network 3D_CNN and key frames extractor algorithm by the fast neural network.
25. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time.
26. This paper presents a solution, generic enough, with the help of machine learning algorithms, allowing its application in a wide range of human-computer interfaces, for real-time gesture recognition.
27. The gesture recognition market is expected to register a CAGR of over 27.9% during the forecast period, 2020-2025.
28. The evolution of GUI technology from the use of texts as inputs to the use of gestures as inputs has paved the way for the emergence of gesture recognition technology.
29. The use of gesture recognition is increasing in various sectors.
30. One recent development in this area is the interaction of humans with machines, by using hand gesture recognition.
31. In this paper, we present a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of an event-based vision sensor and three different neuromorphic processors.
32. Hand-gesture recognition is the process of classifying meaningful gestures of the hands and is currently receiving renewed interest.
33. Hand-gesture recognition systems also target medical applications, where they are detected via bioelectrical signals instead of vision.
34. In this paper we present a fully-neuromorphic implementation of sensor fusion for hand-gesture recognition.
35. AI gesture recognition systems that were initially visual-only have been improved upon by integrating inputs from wearable sensors, an approach known as 'data fusion'.
36. Contains all the GRT context modules, these are modules that can be connected to a gesture recognition pipeline to input additional context to a real-time classification system.
37. Gesture recognition and identification of pose in dynamic environment is done in Python language.
38. Further use of database for hand gesture recognition and their uses are provided in the chapter for real-time implementation on the system to increase the variability use of gesture recognition.
39. BMW’s Series 7 introduced gesture recognition capabilities in 2016.
40. Gesture recognition involves integrating multiple elements.
41. The field of computerized hand-gesture recognition emerged in the early 1980s with the development of wired gloves that integrated sensors on the finger joints, called data gloves.
42. (Step 1) is perhaps the easiest aspect of making gesture recognition systems work.
43. Static Hand Gesture Recognition Using Multi-Layer Neural Network Classifier on Hybrid of Features, American Journal of Intelligent Systems, Vol.
44. Such include Rahman and Afrin (2013) who worked on hand gesture recognition using multi-class support vector machine.
45. (2012) present a hand gesture recognition based on multi-feature fusion.
46. Nagarajan and Subashini (2013) developed a static hand gesture recognition system for sign language alphabets using Edge Oriented Histogram and multi-class SVM.
47. But what then seemed like a pie in the sky, is totally achievable with today’s gesture recognition technology.
48. The new challenge in gesture recognition is how to get rid of gloves altogether.
49. But gesture recognition has always been more challenging and only recently started to gain popularity.
50. Gesture recognition should bring great results no matter the background: it should work whether you’re in the car, at home, or walking down the street.
51. Through advanced AI-enabled gesture recognition, retailers can assess the popularity of an item by learning from shoppers’ facial and hand gestures.
52. The evolution of GUI technology from the use of texts as inputs, to the use of gestures as inputs, has paved the way for the emergence of gesture recognition technology.
53. The gesture recognition usage is increasing, in various sectors.
54. One recent development in this area is the humans interacting with machines, by using hand gesture recognition.
55. A gesture recognition application system comprises several key hardware and software components, all of which must be tightly integrated, to provide a compelling user experience.
56. The design of this efficient human-computer interface for hand gesture recognition is a great area of research which has created employment opportunities for both developers and users.
57. Finally, the designed model stands out as a measure to suggesting alternative resection method in hand gesture recognition saga.
58. It is possible to make a gesture recognition system for these people.
59. With great energy harvesting and human motion sensing capabilities, the glove using the superhydrophobic textile realizes a low‐cost and self‐powered interface for gesture recognition.
60. Differentiated from the abovementioned works, low‐cost and self‐powered glove interfaces based on TENG are developed for complex gesture recognition with the help of machine learning.
61. Finally, 3D VR/AR applications including gun shooting, baseball pitching, and floral arrangement are achieved by gesture recognition based on the glove‐based HMI.
62. By such a demonstration, it is proved that the simple gesture recognition can be a choice for two or more states control.