The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
소실점과 소실선을 검출하는 기존 방법에서는 관찰된 특징점을 서로 다른 선을 나타내는 집합으로 클러스터링합니다. 그런 다음 여러 선이 감지되고 소실점은 선의 교차점으로 감지됩니다. 그러면 교차점을 기준으로 소실선이 감지됩니다. 그러나 최적화를 위해서는 이러한 프로세스가 통합되어 동시에 달성되어야 합니다. 본 논문에서는 특징점에 대해 관측된 노이즈 모델이 2차원 가우스 혼합이라고 가정하고 명백한 소실점과 소실선 매개변수를 포함하는 우도 함수를 정의합니다. 결과적으로, 위에서 설명한 동시 검출은 최대 우도 추정 문제로 정식화될 수 있다. 또한, 이러한 추정을 달성하기 위한 반복 계산 방법은 EM(Expectation Maximization) 알고리즘을 기반으로 제안됩니다. 제안된 방법은 안정적인 수렴을 달성하고 계산 비용을 줄이는 새로운 기술을 포함합니다. 이러한 기법을 포함하는 제안 방법의 유효성은 컴퓨터 시뮬레이션과 실제 이미지를 통해 확인할 수 있다.
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부
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
부
@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},}
부
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 -