Paper Info
Title
Comparison of Feedforward (TDRBF) and Generative (TDRGBN) Network for Gesture Based Control
Abstract
In Visually Mediated Interaction (VMI) there is a range of tasks that need to be supported (face and gesture recognition, camera controlled by gestures, visual interaction etc). These tasks vary in complexity. Generative and self-organising models may offer strong advantages over feedforward ones in cases where a higher degree of generalization is needed. They have the ability to model the density function that generates the data, and this gives the potential of "understanding" the gesture independent from the individual differences on the performance of a gesture. This paper presents a comparison between a feedforward network (RBFN) and a generative one (RGBN) both extended in a time-delay version.
Year
DOI
Venue
2001
10.1007/3-540-47873-6_33
Gesture Workshop
Keywords
Field
DocType
density function,self-organising model,strong advantage,time-delay version,feedforward network,higher degree,visually mediated interaction,gesture recognition,individual difference,supervised learning
Visual interaction,Gesture,Computer science,Gesture recognition,Artificial intelligence,Generative grammar,Probability density function,Feed forward
Conference
ISBN
Citations 
PageRank 
3-540-43678-2
4
0.56
References 
Authors
7
3
Name
Order
Citations
PageRank
Helen Vassilakis140.56
A. Jonathan Howell240.56
Hilary Buxton3491135.93