A fast parametric motion estimation algorithm with illumination and lens distortion correction
Methods for estimating motion in video sequences that are based on the optical flow equation (OFE) assume that the scene illumination is uniform and that the imaging optics are ideal. When these assumptions are appropriate, these methods can be very accurate, but when they are not, the accuracy of t...
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| Veröffentlicht in: | IEEE transactions on image processing 2003-04, Vol.12 (4), p.395-408 |
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| container_title | IEEE transactions on image processing |
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| creator | Altunbasak, Y. Mersereau, R.M. Patti, A.J. |
| description | Methods for estimating motion in video sequences that are based on the optical flow equation (OFE) assume that the scene illumination is uniform and that the imaging optics are ideal. When these assumptions are appropriate, these methods can be very accurate, but when they are not, the accuracy of the motion field drops off accordingly. This paper extends the models upon which the OFE methods are based to include irregular, time-varying illumination models and models for imperfect optics that introduce vignetting, gamma, and geometric warping, such as are likely to be found with inexpensive PC cameras. The resulting optimization framework estimates the motion parameters, illumination parameters, and camera parameters simultaneously. In some cases these models can lead to nonlinear equations which must be solved iteratively; in other cases, the resulting optimization problem is linear. For the former case an efficient, hierarchical, iterative framework is provided that can be used to implement the motion estimator. |
| doi_str_mv | 10.1109/TIP.2003.809012 |
| format | Article |
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When these assumptions are appropriate, these methods can be very accurate, but when they are not, the accuracy of the motion field drops off accordingly. This paper extends the models upon which the OFE methods are based to include irregular, time-varying illumination models and models for imperfect optics that introduce vignetting, gamma, and geometric warping, such as are likely to be found with inexpensive PC cameras. The resulting optimization framework estimates the motion parameters, illumination parameters, and camera parameters simultaneously. In some cases these models can lead to nonlinear equations which must be solved iteratively; in other cases, the resulting optimization problem is linear. 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When these assumptions are appropriate, these methods can be very accurate, but when they are not, the accuracy of the motion field drops off accordingly. This paper extends the models upon which the OFE methods are based to include irregular, time-varying illumination models and models for imperfect optics that introduce vignetting, gamma, and geometric warping, such as are likely to be found with inexpensive PC cameras. The resulting optimization framework estimates the motion parameters, illumination parameters, and camera parameters simultaneously. In some cases these models can lead to nonlinear equations which must be solved iteratively; in other cases, the resulting optimization problem is linear. 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When these assumptions are appropriate, these methods can be very accurate, but when they are not, the accuracy of the motion field drops off accordingly. This paper extends the models upon which the OFE methods are based to include irregular, time-varying illumination models and models for imperfect optics that introduce vignetting, gamma, and geometric warping, such as are likely to be found with inexpensive PC cameras. The resulting optimization framework estimates the motion parameters, illumination parameters, and camera parameters simultaneously. In some cases these models can lead to nonlinear equations which must be solved iteratively; in other cases, the resulting optimization problem is linear. For the former case an efficient, hierarchical, iterative framework is provided that can be used to implement the motion estimator.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>18237918</pmid><doi>10.1109/TIP.2003.809012</doi><tpages>14</tpages></addata></record> |
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| issn | 1057-7149 1941-0042 |
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| subjects | Algorithms Applied sciences Cameras Detection, estimation, filtering, equalization, prediction Estimating Exact sciences and technology Flow equations Illumination Image motion analysis Image processing Information, signal and communications theory Lenses Lighting Mathematical models Motion estimation Nonlinear distortion Nonlinear optics Optical distortion Optimization Signal and communications theory Signal processing Signal, noise Solid modeling Studies Telecommunications and information theory Video sequences Vignetting |
| title | A fast parametric motion estimation algorithm with illumination and lens distortion correction |
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