Paper Info
Title
Gesture recognition using the multi-PDM method and hidden Markov model
Abstract
This paper introduces a multi-Principal-Distribution-Model (PDM) method and Hidden Markov Model (HMM) for gesture recognition. To track the hand-shape, it uses the PDM model which is built by learning patterns of variability from a training set of correctly annotated images. However, it can only fit the hand examples that are similar to shapes of the corresponding training set. For gesture recognition, we need to deal with a large variety of hand-shapes. Therefore, we divide all the training hand shapes into a number of similar groups, with each group trained for an individual PDM shape model. Finally, we use the HMM to determine model transition among these PDM shape models. From the model transition sequence, the system can identify the continuous gestures representing one-digit or two-digit numbers.
Year
DOI
Venue
2000
10.1016/S0262-8856(99)00042-6
Image and Vision Computing
Keywords
Field
DocType
Gesture recognition,Multi-PDM method,Hidden Markov model
Training set,Active shape model,Pattern recognition,Gesture,Computer science,Gesture recognition,Speech recognition,Artificial intelligence,Hidden Markov model
Journal
Volume
Issue
ISSN
18
11
0262-8856
Citations 
PageRank 
References 
6
0.49
8
Authors
3
Name
Order
Citations
PageRank
Chung-Lin Huang113913.24
Ming-Shan Wu260.83
Sheng-Hung Jeng3362.12