A two-camera based algorithm for 3D reconstruction of a visual scene. Probabilistic approach
One of the approaches to the problem of 3D reconstruction, based on the probabilistic identification of matched images patches, without using active illumination sources, is addressed. The source data for the algorithm are the vectors that describe the boundaries of image regions obtained in advance...
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| Published in | Journal of computer & systems sciences international Vol. 48; no. 1; pp. 131 - 138 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
SP MAIK Nauka/Interperiodica
01.02.2009
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1064-2307 1555-6530 |
| DOI | 10.1134/S1064230709010122 |
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| Abstract | One of the approaches to the problem of 3D reconstruction, based on the probabilistic identification of matched images patches, without using active illumination sources, is addressed. The source data for the algorithm are the vectors that describe the boundaries of image regions obtained in advance by a procedure of segmentation into parts of a constant color and the subsequent vectorization of the boundaries of regions. The method described in the paper can deal not only with polyhedrons, but also with objects of arbitrary shape. |
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| AbstractList | One of the approaches to the problem of 3D reconstruction, based on the probabilistic identification of matched images patches, without using active illumination sources, is addressed. The source data for the algorithm are the vectors that describe the boundaries of image regions obtained in advance by a procedure of segmentation into parts of a constant color and the subsequent vectorization of the boundaries of regions. The method described in the paper can deal not only with polyhedrons, but also with objects of arbitrary shape. One of the approaches to the problem of 3D reconstruction, based on the probabilistic identification of matched images patches, without using active illumination sources, is addressed. The source data for the algorithm are the vectors that describe the boundaries of image regions obtained in advance by a procedure of segmentation into parts of a constant color and the subsequent vectorization of the boundaries of regions. The method described in the paper can deal not only with polyhedrons, but also with objects of arbitrary shape.[PUBLICATION ABSTRACT] |
| Author | Shcherbakov, A. P. |
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| DOI | 10.1134/S1064230709010122 |
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| Keywords | Terminal Point Visual Scene Stereo Pair System Science International Image Region |
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| References | V. Kyrki, I. Kuzivanov, J. Vartiainen, et al., “3D Reconstruction of Polyhedral Objects with Partial Occlusion,” in Proceedings of 7th International Conference on Pattern Recognition and Image Analysis, PRIA-7-2004, St. Petersburg, Russia, Vol. 3, p. 764. A. P. Shcherbakov, “A High-Performance Image Segmentation Algorithm,” Avtometriya, No. 2, 59–67 (2005). GonzalesR. C.WoodsR. E.Digital Image Processing20022New JerseyPrentice Hall A. A. Borovkov, Mathematical Statistics (Nauka, Novosibirsk) [in Russian]. N. V. Milich and V. N. Milich, “Methods for Reconstruction of Surfaces based on 3D Scanning,” Informatsionnye Tekhnologii Modelirovaniya i Upravleniya, No. 3(28), 339–344 (2006). A. P. Shcherbakov, “A High-Performance Algorithm for Calculating the Location, Angle of Rotation, and Dimensions of an Object-Sample on a Two-Dimensional Visual Scene,” Tsifrovaya Obrabotka Signalov, No. 4, 15–20 (2004). AmerbaevV. M.KrenkelT. E.Fourier Transform and Algorithms for Calculating Convolutions1985MoscowRadio i Svyaz HornB. K.Robot Vision1985New YorkMcGraw-Hill AizermanM. A.BravermanE. I.RozonoerL. I.Method of Potential Functions in Machine Learning Problems1970MoscowNauka M. A. Aizerman (1012_CR5) 1970 1012_CR9 1012_CR6 1012_CR4 1012_CR2 1012_CR3 B. K. Horn (1012_CR1) 1985 R. C. Gonzales (1012_CR7) 2002 (1012_CR8) 1985 |
| References_xml | – reference: A. P. Shcherbakov, “A High-Performance Image Segmentation Algorithm,” Avtometriya, No. 2, 59–67 (2005). – reference: HornB. K.Robot Vision1985New YorkMcGraw-Hill – reference: AizermanM. A.BravermanE. I.RozonoerL. I.Method of Potential Functions in Machine Learning Problems1970MoscowNauka – reference: V. Kyrki, I. Kuzivanov, J. Vartiainen, et al., “3D Reconstruction of Polyhedral Objects with Partial Occlusion,” in Proceedings of 7th International Conference on Pattern Recognition and Image Analysis, PRIA-7-2004, St. Petersburg, Russia, Vol. 3, p. 764. – reference: GonzalesR. C.WoodsR. E.Digital Image Processing20022New JerseyPrentice Hall – reference: AmerbaevV. M.KrenkelT. E.Fourier Transform and Algorithms for Calculating Convolutions1985MoscowRadio i Svyaz’ – reference: A. A. Borovkov, Mathematical Statistics (Nauka, Novosibirsk) [in Russian]. – reference: N. V. Milich and V. N. Milich, “Methods for Reconstruction of Surfaces based on 3D Scanning,” Informatsionnye Tekhnologii Modelirovaniya i Upravleniya, No. 3(28), 339–344 (2006). – reference: A. P. Shcherbakov, “A High-Performance Algorithm for Calculating the Location, Angle of Rotation, and Dimensions of an Object-Sample on a Two-Dimensional Visual Scene,” Tsifrovaya Obrabotka Signalov, No. 4, 15–20 (2004). – ident: 1012_CR2 – ident: 1012_CR3 – volume-title: Method of Potential Functions in Machine Learning Problems year: 1970 ident: 1012_CR5 – volume-title: Fourier Transform and Algorithms for Calculating Convolutions year: 1985 ident: 1012_CR8 – volume-title: Digital Image Processing year: 2002 ident: 1012_CR7 – volume-title: Robot Vision year: 1985 ident: 1012_CR1 – ident: 1012_CR4 – ident: 1012_CR6 – ident: 1012_CR9 |
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| Title | A two-camera based algorithm for 3D reconstruction of a visual scene. Probabilistic approach |
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