Paper Info
Title
Application of Bayesian network for fuzzy rule-based video deinterlacing
Abstract
This paper proposes a fuzzy reasoning interpolation method for video deinterlacing. We propose edge detection parameters to measure the amount of entropy in the spatial and temporal domains. The shape of the membership functions is designed adaptively, according to those parameters and can be utilized to determine edge direction. Our proposed fuzzy edge direction detector operates by identifying small pixel variations in nine orientations in each domain and uses rules to infer the edge direction. We employ a Bayesian network, which provides accurate weightings between the proposed deinterlacing method and common existing deinterlacing methods. It successively builds approximations of the deinterlaced sequence by weighting interpolation methods. The results of computer simulations show that the proposed method outperforms a number of methods in the literature.
Year
DOI
Venue
2007
10.1007/978-3-540-77129-6_73
PSIVT
Keywords
Field
DocType
video deinterlacing,fuzzy reasoning interpolation method,edge direction,bayesian network,fuzzy rule-based video deinterlacing,proposed deinterlacing method,proposed fuzzy edge direction,edge detection parameter,weighting interpolation method,common existing deinterlacing method,membership function,edge detection,computer simulation
Computer vision,Weighting,Pattern recognition,Deinterlacing,Computer science,Edge detection,Fuzzy logic,Interpolation,Bayesian network,Artificial intelligence,Pixel,Fuzzy rule
Conference
Volume
ISSN
ISBN
4872
0302-9743
3-540-77128-X
Citations 
PageRank 
References 
0
0.34
2
Authors
5
Name
Order
Citations
PageRank
Gwanggil Jeon1596117.99
Rafael Falcon211316.51
Rafael Bello330825.28
Dong-Hyung Kim413014.38
Jechang Jeong51002141.22