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Gesture recognition is an area of research and development in computer science and language technology concerned with the recognition and interpretation of human gestures. A subdiscipline of computer vision , [ citation needed ] it employs mathematical algorithms to interpret gestures.
Finger tracking of two pianists' fingers playing the same piece (slow motion, no sound) [1]. In the field of gesture recognition and image processing, finger tracking is a high-resolution technique developed in 1969 that is employed to know the consecutive position of the fingers of the user and hence represent objects in 3D.
7805 gesture captures of 14 different social touch gestures performed by 31 subjects. The gestures were performed in three variations: gentle, normal and rough, on a pressure sensor grid wrapped around a mannequin arm. Touch gestures performed are segmented and labeled. 7805 gesture captures CSV Classification 2016 [194] [195] M. Jung et al.
Object recognition, scene recognition 2014 [15] [16] J. Xiao et al. ImageNet: Labeled object image database, used in the ImageNet Large Scale Visual Recognition Challenge: Labeled objects, bounding boxes, descriptive words, SIFT features 14,197,122 Images, text Object recognition, scene recognition 2009 (2014) [17] [18] [19] J. Deng et al. LSUN
Modern devices are being experimented with, that may potentially allow that computer related device to respond to and understand an individual's hand gesture, specific movement or facial expression. In relation to computers and body language, research is being done with the use of mathematics in order to teach computers to interpret human ...
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
The first commercially available ultrasound device was the Stratos Explore by Ultrahaptics that consisted of 256-transducer array board and a Leap motion controller for hand tracking [32] Another form of tactile feed back results from active touch when a human scans (runs their finger over a surface) to gain information about a surfaces texture.
SixthSense is a gesture-based wearable computer system developed at MIT Media Lab by Steve Mann in 1994 and 1997 (headworn gestural interface), and 1998 (neckworn version), and further developed by Pranav Mistry (also at MIT Media Lab), in 2009, both of whom developed both hardware and software for both headworn and neckworn versions of it.