Vanishing point and vanishing line estimation with line clustering

Citation
A. Minagawa et al., Vanishing point and vanishing line estimation with line clustering, IEICE T INF, E83D(7), 2000, pp. 1574-1582
Citations number
9
Categorie Soggetti
Information Tecnology & Communication Systems
Journal title
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
ISSN journal
09168532 → ACNP
Volume
E83D
Issue
7
Year of publication
2000
Pages
1574 - 1582
Database
ISI
SICI code
0916-8532(200007)E83D:7<1574:VPAVLE>2.0.ZU;2-1
Abstract
In conventional methods for detecting vanishing points and vanishing lines, the observed feature points are clustered into collections that represent different lines. The multiple lines are then detected and the vanishing poi nts are detected as points of intersection of the lines. The vanishing line is then detected based on the points of intersection. However, for the pur pose of optimization, these processes should be integrated and be achieved simultaneously. In the present paper, we assume that the observed noise mod el for the feature points is a two-dimensional Gaussian mixture and define the likelihood function, including obvious vanishing points and a vanishing line parameters. As a result, the above described simultaneous detection c an be formulated as a maximum likelihood estimation problem. In addition, a n iterative computation method for achieving this estimation is proposed ba sed on the Ehl (Expectation Maximization) algorithm. The proposed method in volves new techniques by which stable convergence is achieved and computati onal cost is reduced. The effectiveness of the proposed method that include s these techniques can be confirmed by computer simulations and real images .