Title
A hand shape instruction recognition and learning system using growing SOM with asymmetric neighborhood function
Abstract
For Human–Machine Interaction systems, it is a convenient method to send user׳s instructions to robots, TV sets, and other electronic equipments by showing different shapes of a hand of user. In our previous works, we proposed to use improved Kohonen׳s Self-Organizing Maps (SOMs), i.e., Transient-SOM (T-SOM) and Parameterless Growing SOM (PL-G-SOM) to recognize different patterns of hand shapes given by different bendings of five fingers of a hand. Recently, an asymmetric neighborhood function was proposed and introduced into the conventional SOM to improve the learning performance by Aoki and Aoyagi. In this paper, we propose to employ their asymmetric neighborhood function into Growing SOM (GSOM), which is an improved SOM to deal with additional online learning for input data. Furthermore, the improved GSOM is applied to a hand shape recognition and instruction learning system, and the results of experiments with eight kinds of instructions showed the effectiveness of the proposed system.
Year
DOI
Venue
2014
10.1016/j.neucom.2014.10.108
Neurocomputing
Keywords
Field
DocType
Self-Organizing Map,Neighborhood function,Human–Machine Interaction,Instruction learning system
Computer science,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
188
0925-2312
1
PageRank 
References 
Authors
0.39
20
5
Name
Order
Citations
PageRank
Takashi Kuremoto119627.73
Takuhiro Otani210.72
Masanao Obayashi319826.10
Kunikazu Kobayashi417321.96
Shingo Mabu549377.00