Paper Info
Title
High Accuracy Computation of Rank-Constrained Fundamental Matrix
Abstract
A new method is presented for computing the fundamental matrix from point correspondences: its singular value decomposition (SVD) is optimized by the Levenberg-Marquard (LM) method. The search is initialized by opti- mal correction of unconstrained ML. There is no need for tentative 3-D re- construction. The accuracy achieves the theoretical bound (the KCR lower bound).
Year
Venue
Keywords
2007
BMVC
fundamental matrix,lower bound,singular value decomposition
Field
DocType
Citations 
Singular value decomposition,Mathematical optimization,Pattern recognition,Computer science,Upper and lower bounds,Algorithm,Artificial intelligence,Fundamental matrix (computer vision),Computation
Conference
7
PageRank 
References 
Authors
0.49
11
2
Name
Order
Citations
PageRank
Yasuyuki Sugaya126725.45
Kenichi Kanatani21468320.07