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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.
Sketch recognition describes the process by which a computer, or artificial intelligence can interpret hand-drawn sketches created by a human being, or other machine. [1] Sketch recognition is a key frontier in the field of artificial intelligence and human-computer interaction , similar to natural language processing or conversational ...
Implementations which allow the user to sketch a picture on the system's table top with a real tangible pen. Using hand gestures, the user can clone the image and stretch it in the X and Y axes just as one would in a paint program. This system would integrate a video camera with a gesture recognition system. jive. The implementation of a TUI ...
Gestures could be efficiently used as a means of detecting a particular emotional state of the user, especially when used in conjunction with speech and face recognition. Depending on the specific action, gestures could be simple reflexive responses, like lifting your shoulders when you don't know the answer to a question, or they could be ...
In 1990, Sears et al. published a review of academic research on single and multi-touch touchscreen human–computer interaction of the time, describing single touch gestures such as rotating knobs, swiping the screen to activate a switch (or a U-shaped gesture for a toggle switch), and touchscreen keyboards (including a study that showed that ...
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. SIFT keypoints of objects are first extracted from a set of reference images [1] and stored in a database.
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.
Furthermore, his systematic study on the recognition of 3D gestures involved creating a database of 25 distinct gestures and analyzing the impact of training sample size showcasing that both linear and AdaBoost classifiers can achieve over 90% accuracy in recognizing up to 25 gestures.