29-08-2014, 10:33 AM
A Hand Gesture Recognition System Based on Local Linear Embedding
A Hand Gesture.ppt (Size: 293 KB / Downloads: 7)
Introduction
Interaction with computers are not comfortable experience
Computers should communicate with people with body language.
Hand gesture recognition becomes important
Interactive human-machine interface and virtual environment
Two common technologies for hand gesture recognition
glove-based method
Using special glove-based device to extract hand posture
Annoying
vision-based method
3D hand/arm modeling
Appearance modeling
Overview of algorithm proposed in the paper
Vision-based method to be used for the problem of CSL real-time recognition
Input: 2D video sequences
two major steps
Hand gesture region detection
Hand gesture recognition
CSL and Pre-processing
Sign Language
Rely on the hearing society
Two main elements:
Low and simple level signed alphabet, mimics the letters of the native spoken language
Higher level signed language, using actions to mimic the meaning or description of the sign
Pre-processing of Hand Gesture Recognition
Detection of Hand Gesture Regions
Aim to fix on the valid frames and locate the hand region from the rest of the image.
Low time consuming fast processing rate real time speed
Pre-processing of Hand Gesture Recognition
Detect skin region from the rest of the image by using color.
Each color has three components
hue, saturation, and value
chroma consists of hue and saturation is separated from value
Under different condition, chroma is invariant.
Locally Linear Embedding
Sparse data vs. High dimensional space
30 different gestures, 120 samples/gesture
36*36 pixels
3600 training samples vs. d = 1296
Difficult to describe the data distribution
Reduce the dimensionality of hand gesture images
Locally Linear Embedding
Find the k nearest neighbours of each point xi
Measure reconstruction error from the approximation of each point by the neighbour points and compute the reconstruction weights which minimize the error
Compute the low-embedding by minimizing an embedding cost function with the reconstruction weights
Conclusion
Robust against similar postures in different light conditions and backgrounds
Fast detection process, allows the real time video application with low cost sensors, such as PC and USB camera