Vanishing Point and Vanishing Line Estimation with Line Clustering

Akihiro MINAGAWA; Norio TAGAWA; Tadashi MORIYA; Toshiyuki GOTOH

Vanishing Point and Vanishing Line Estimation with Line Clustering

Akihiro MINAGAWA, Norio TAGAWA, Tadashi MORIYA, Toshiyuki GOTOH

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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 points are detected as points of intersection of the lines. The vanishing line is then detected based on the points of intersection. However, for the purpose of optimization, these processes should be integrated and be achieved simultaneously. In the present paper, we assume that the observed noise model 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 can be formulated as a maximum likelihood estimation problem. In addition, an iterative computation method for achieving this estimation is proposed based on the EM (Expectation Maximization) algorithm. The proposed method involves new techniques by which stable convergence is achieved and computational cost is reduced. The effectiveness of the proposed method that includes these techniques can be confirmed by computer simulations and real images.

Publication: IEICE TRANSACTIONS on Information Vol.E83-D No.7 pp.1574-1582

Publication Date: 2000/07/25

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Type of Manuscript: PAPER

Category: Image Processing, Image Pattern Recognition

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Akihiro MINAGAWA, Norio TAGAWA, Tadashi MORIYA, Toshiyuki GOTOH, "Vanishing Point and Vanishing Line Estimation with Line Clustering" in IEICE TRANSACTIONS on Information, vol. E83-D, no. 7, pp. 1574-1582, July 2000, doi: .
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 points are detected as points of intersection of the lines. The vanishing line is then detected based on the points of intersection. However, for the purpose of optimization, these processes should be integrated and be achieved simultaneously. In the present paper, we assume that the observed noise model 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 can be formulated as a maximum likelihood estimation problem. In addition, an iterative computation method for achieving this estimation is proposed based on the EM (Expectation Maximization) algorithm. The proposed method involves new techniques by which stable convergence is achieved and computational cost is reduced. The effectiveness of the proposed method that includes these techniques can be confirmed by computer simulations and real images.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_7_1574/_p

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@ARTICLE{e83-d_7_1574,
author={Akihiro MINAGAWA, Norio TAGAWA, Tadashi MORIYA, Toshiyuki GOTOH, },
journal={IEICE TRANSACTIONS on Information},
title={Vanishing Point and Vanishing Line Estimation with Line Clustering},
year={2000},
volume={E83-D},
number={7},
pages={1574-1582},
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 points are detected as points of intersection of the lines. The vanishing line is then detected based on the points of intersection. However, for the purpose of optimization, these processes should be integrated and be achieved simultaneously. In the present paper, we assume that the observed noise model 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 can be formulated as a maximum likelihood estimation problem. In addition, an iterative computation method for achieving this estimation is proposed based on the EM (Expectation Maximization) algorithm. The proposed method involves new techniques by which stable convergence is achieved and computational cost is reduced. The effectiveness of the proposed method that includes these techniques can be confirmed by computer simulations and real images.},
keywords={},
doi={},
ISSN={},
month={July},}

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TY - JOUR
TI - Vanishing Point and Vanishing Line Estimation with Line Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 1574
EP - 1582
AU - Akihiro MINAGAWA
AU - Norio TAGAWA
AU - Tadashi MORIYA
AU - Toshiyuki GOTOH
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2000
AB - 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 points are detected as points of intersection of the lines. The vanishing line is then detected based on the points of intersection. However, for the purpose of optimization, these processes should be integrated and be achieved simultaneously. In the present paper, we assume that the observed noise model 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 can be formulated as a maximum likelihood estimation problem. In addition, an iterative computation method for achieving this estimation is proposed based on the EM (Expectation Maximization) algorithm. The proposed method involves new techniques by which stable convergence is achieved and computational cost is reduced. The effectiveness of the proposed method that includes these techniques can be confirmed by computer simulations and real images.
ER -