Abstract principal component analysis [6], Fourier descriptors, neural networks, orientation histograms [7], and particle filters [8] . We propose a fast algorithm for automatically recognizing a limited set of gestures from hand images for Our focus the recognition of a fixed set of manual a robot control application. Hand gesture recognition is a commands by a robot, in a reasonably structured environment challenging problem in its general form. We consider a in real time. Therefore the speed, hence simplicity of the fixed set of manual commands and a reasonably structured algorithm is important. We develop and implement such a environment, and develop a simple, yet effective, procedure inthis work. Our approach involves segmenting the procedure for gesture recognition. Our approach contains hand based on skin color statistics, as well as size constraints. steps for segmenting the hand region, locating the fingers, We then find the center of gravity (COG) of the hand region and finally classifying the gesture. The algorithm is as well the farthest point from the COG. Based on these preinvariant to translation, rotation, and scale of the hand. processing steps, we derive a signal that carries information on We demonstrate the effectiveness of the technique on real the activity of the fingers in the sign. Finally we identify the imagery. sign based on that signal. Our algorithm is invariant to rotations, translations and scale of the hand. Furthermore, the technique does not require the storage of a hand gesture
Malima REF proposed an algorithm that automatically identifies a limited set of hand gestures from images used for robot control to perform tasks.
A Fast Algorithm for Vision-Based Hand Gesture Recognition for Robot Control
2006 IEEE 14th Signal Processing and Communications Applications
2006 IEEE 14th Signal Processing and Communications Applications
Computer Science