Cart (Loading....) | Create Account
Close category search window
 

Camera model selection based on geometric AIC

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kinoshita, K. ; Res. Labs., ATR Human Inf. Processing, Kyoto, Japan ; Lindenbaum, L.

The problem of selecting a camera model is addressed here using the Geometric AIC (Akaike Information Criterion) proposed by Kanatani, which considers both the residual of the data fitting to the model as well as the complexity of the model. Camera models describe the geometrical relation between the 3D location of object points and the image location of their projections. The most commonly used camera models are the projective/perspective camera model and the affine camera model. Intuitively, the projective camera model, which is nonlinear and is characterized by more parameters, models the imaging geometry better, but also, is believed to lead to numerically less stable solutions. The affine camera model, which is an approximation to the projective camera model with less parameters, is recommended to be used when the object depth is much smaller than the object distance. However, there is no quantitative criterion for the decision: which camera model should be used, projective or affine? In this paper, the Geometric AIC criterion is used for deciding between the two camera models is the context of two tasks: estimating the projection matrix from 3D and corresponding 2D data, and estimating the fundamental matrix from two sets of 3D data. It is found that in most cases, it is the projective camera model which is more appropriate. Still, in the cases where the affine camera model is traditionally used, the measures of appropriateness of the two models are roughly the same (with a small advantage to the affine camera model)

Published in:

Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on  (Volume:2 )

Date of Conference:

2000

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.