Paper Info
Title
Rigid 3-D motion estimation using neural networks and initially estimated 2-D motion data
Abstract
This paper extends a known efficient technique for rigid three-dimensional (3-D) motion estimation so as to make it applicable to motion estimation problems occuring in image sequence coding applications. The known technique estimates 3-D motion using previously evaluated 3-D correspondence. However, in image sequence coding applications, 3-D correspondence is unknown and usually only two-dimensional (2-D) motion vectors are initially available. The novel neural network (NN) introduced in this paper uses initially estimated 2-D motion vectors to estimate 3-D rigid motion, and is therefore suitable for image sequence coding applications. Moreover, it is shown that the NN introduced in this paper performs extremely well even in cases where 3-D correspondence is known with accuracy. Experimental results are presented for the evaluation of the proposed scheme
Year
DOI
Venue
2000
10.1109/76.825869
Circuits and Systems for Video Technology, IEEE Transactions
Keywords
Field
DocType
neural network,image sequence,3-d rigid motion,efficient technique,motion estimation,motion estimation problem,3-d correspondence,rigid 3-d motion estimation,known technique,2-d motion vector,3-d motion,motion vector,2-d motion data,information processing,neural networks,three dimensional,application software,neural nets,layout
Iterative reconstruction,Computer vision,Quarter-pixel motion,Motion field,Jacobian matrix and determinant,Pattern recognition,Computer science,Image processing,Artificial intelligence,Estimation theory,Motion estimation,Artificial neural network
Journal
Volume
Issue
ISSN
10
1
1051-8215
Citations 
PageRank 
References 
5
0.53
12
Authors
3
Name
Order
Citations
PageRank
Dimitrios Tzovaras11377205.82
Ploskas, N.2101.22
M. G. Strintizis350.53