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This article is cited in 4 scientific papers (total in 4 papers)
Programming and Computer Software
Calculation of a vanishing point by the maximum likelihood estimation method
I. A. Konovalenkoa, J. A. Shemiakinab, I. A. Faradjevb a The Institute for Information Transmission Problems of Russian Academy of Sciences, Moscow, Russian Federation
b Federal Research Center “Computer Science and Control” of Russian Academy
of Sciences, Moscow, Russian Federation
Abstract:
The paper presents a method to estimate the position of the vanishing point of a set of converging noisy segments. As a model of segment noise, we use normal noise applied to the end points of the segment. We construct a functional that depends on the position of the considered segments and determine the vanishing point as the point at which the functional reaches its minimum. In order to set such a functional, we use the maximum likelihood estimation method. The obtained functional allows both to pose the problem on numerical minimization and to set the function of correspondence of the segment to the beam, if it is necessary to filter out the emission segments from the initial set. The application of the proposed method is illustrated by the example of correction of projective distortions and the subsequent localization of the pages of the Russian Federation passport on the images of the passport pages. In the framework of this problem, we assume that the segments highlighted in an image correspond to several beams and contain emissions. In order to ensure stability and ability to estimate a set of vanishing points, we propose an algorithm based on the RANSAC scheme. The use of the projective normalization method allows to reduce the number of page localization errors from 6,4% to 1,8%.
Keywords:
vanishing point, maximum likelihood estimation method, segments, image rectification.
Received: 21.11.2019
Citation:
I. A. Konovalenko, J. A. Shemiakina, I. A. Faradjev, “Calculation of a vanishing point by the maximum likelihood estimation method”, Vestnik YuUrGU. Ser. Mat. Model. Progr., 13:1 (2020), 107–117
Linking options:
https://www.mathnet.ru/eng/vyuru534 https://www.mathnet.ru/eng/vyuru/v13/i1/p107
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Abstract page: | 93 | Full-text PDF : | 32 | References: | 18 |
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