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Database of grayscale handwritten digits. 60,000 image, label classification 1994 [1] LeCun et al. Extended MNIST: Database of grayscale handwritten digits and letters. 810,000 image, label classification 2010 [2] NIST 80 Million Tiny Images: 80 million 32×32 images labelled with 75,062 non-abstract nouns. 80,000,000 image, label 2008 [3 ...
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 [195] [196] M. Jung et al.
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.
The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999. [1] Applications include object recognition , robotic mapping and navigation, image stitching , 3D modeling , gesture recognition , video tracking , individual identification of ...
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.
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]
A computer should be able to recognize these, analyze the context and respond in a meaningful way, in order to be efficiently used for Human–Computer Interaction. There are many proposed methods [38] to detect the body gesture. Some literature differentiates 2 different approaches in gesture recognition: a 3D model based and an appearance ...
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3]