Paper Info
Title
A linear iterative least-squares method for estimating the fundamental matrix.
Abstract
During the last two decades a lot of researches have been done on the estimation of fundamental matrix, which represents the epipolar geometry between two un-calibrated perspective images. In this paper, a new linear and iterative method is proposed for estimating the fundamental matrix. It preserves the noise model of the observed image points, e.g. a Gaussian noise distribution. When the noise in the measurement of the image points is small, the accuracy of this method is comparable to that of the non-linear Newtontype optimizers, however, it is much more efficient both because of its linearity and because of its faster convergency.
Year
DOI
Venue
2003
10.1109/ISSPA.2003.1224629
SEVENTH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, VOL 1, PROCEEDINGS
Keywords
Field
DocType
linearity,gaussian distribution,fundamental matrix,iteration method,image processing,least square method,layout,epipolar geometry,gaussian noise,image point,computational geometry,iterative methods,computer science,cost function,newton method,noise measurement
Least squares,Noise measurement,Computer science,Image processing,Artificial intelligence,Fundamental matrix (computer vision),Newton's method,Mathematical optimization,Pattern recognition,Epipolar geometry,Iterative method,Algorithm,Gaussian noise
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Bing Liu15611.41
Reinhard Männer2801536.47