Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual user interfaces (introduction)
Communications of the ACM
A k-Nearest-Neighbour Method for Classifying Web Search Results with Data in Folksonomies
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Real time object recognition using K-nearest neighbor in parametric eigenspace
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Real time face tracking with pyramidal Lucas-Kanade feature tracker
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Object recognition using k-nearest neighbor in object space
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
Human computer interaction with hand gestures in virtual environment
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
| Hi-index | 0.00 |
In daily life, human beings communicate with each other and use broad range of gestures in the process of interaction. Apart of the interpersonal communication, many hours are spent in the interaction with the electronic devices. In the last decade, new classes of devices for accessing information have emerged along with increased connectivity. In parallel to the proliferation of these devices, new interaction styles have been explored. The objective of this paper is to provide a gesture based interface for controlling applications like media player using computer vision techniques. The human computer interface application consists of a central computational module which uses the Principal Component Analysis for gesture images and finds the feature vectors of the gesture and save it into a XML file. The Recognition of the gesture is done by K Nearest Neighbour algorithm. The Training Images are made by cropping the hand gesture from static background by detecting the hand motion using Lucas Kanade Pyramidical Optical Flow algorithm. This hand gesture recognition technique will not only replace the use of mouse to control the media player but also provide different gesture commands which will be useful in controlling the application.