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- W179570882 abstract "The objective of camera calibration is to obtain the correlation between camera image coordinate and 3D real world coordinate. In this paper, we propose a new approach which is based on the neural network model instead of the physical camera model including position, orientation, focal length, and optical center. The neural network employed in this paper is MLPNN (MultiLayer Perceptron Type Neural Network), which is primarily used as a mapper between 2D image points and points of a certain space in 3D real world. The neural network model implicitly contains all the physical parameters, some of which are very difficult to be estimated in the conventional calibration methods. In order to show the performance of the proposed method, images from two different cameras with three different camera angles were used for calibrating the cameras. The performance of the proposed neural network approach is compared with the well-known Tsai’s two stage method in terms of calibration errors. The results show that the proposed approach gives much more consistent and acceptable calibration error over Tsai’s two stage method regardless of the quality of camera and the camera angles." @default.
- W179570882 created "2016-06-24" @default.
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- W179570882 date "2009-01-01" @default.
- W179570882 modified "2023-09-26" @default.
- W179570882 title "Application of MultiLayer Perceptron Type Neural Network to Camera Calibration" @default.
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- W179570882 doi "https://doi.org/10.1007/978-3-642-03156-4_15" @default.
- W179570882 hasPublicationYear "2009" @default.
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