Paper Info
Title
An Iterative Kalman Filter Approach to Camera Calibration
Abstract
An iterative camera calibration approach is presented in this paper. This approach allows computing the optimal camera parameters for a given set of data. If non linear estimation process is done, a risk of reaching a local minimum exists. With this method this risk is reduced and a best estimation is achieved. By one hand, an iterative improving of the estimated camera parameters is done maximizing a posteriori probability density function (PDF) for a given set of data. To resolve it, a Kalman filter is used based on the Bayesian standpoint. Each update is carried out starting with a new set of data, its covariance matrix and a previous estimation of the parameters. In this case, a different management of the input data is done to extract all its information. By the other hand, apart from the calibration algorithm, a method to compute an interval which contains camera parameters is presented. It is based on computing the covariance matrix of the estimated camera parameters.
Year
DOI
Venue
2008
10.1007/978-3-540-88458-3_13
ACIVS
Keywords
Field
DocType
estimated camera parameter,iterative kalman filter approach,covariance matrix,input data,iterative camera calibration approach,camera parameter,optimal camera parameter,non linear estimation process,new set,camera calibration,previous estimation,best estimation,sampling,probability density function,kalman filter,interval computation
Computer vision,Extended Kalman filter,Computer science,Kalman filter,Camera resectioning,Artificial intelligence,Sampling (statistics),Covariance matrix,Interval arithmetic,Probability density function,Bayesian probability
Conference
Volume
ISSN
Citations 
5259
0302-9743
0
PageRank 
References 
Authors
0.34
6
2
Name
Order
Citations
PageRank
Carlos Ricolfe-Viala1414.63
Antonio-José Sánchez-Salmerón2414.63