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http://dx.doi.org/10.5369/JSST.2019.28.3.182

Camera Calibration Using Neural Network with a Small Amount of Data  

Do, Yongtae (School of Electronic & Electrical Engineering, Daegu University)
Publication Information
Journal of Sensor Science and Technology / v.28, no.3, 2019 , pp. 182-186 More about this Journal
Abstract
When a camera is employed for 3D sensing, accurate camera calibration is vital as it is a prerequisite for the subsequent steps of the sensing process. Camera calibration is usually performed by complex mathematical modeling and geometric analysis. On the other contrary, data learning using an artificial neural network can establish a transformation relation between the 3D space and the 2D camera image without explicit camera modeling. However, a neural network requires a large amount of accurate data for its learning. A significantly large amount of time and work using a precise system setup is needed to collect extensive data accurately in practice. In this study, we propose a two-step neural calibration method that is effective when only a small amount of learning data is available. In the first step, the camera projection transformation matrix is determined using the limited available data. In the second step, the transformation matrix is used for generating a large amount of synthetic data, and the neural network is trained using the generated data. Results of simulation study have shown that the proposed method as valid and effective.
Keywords
Camera calibration; Artificial neural network; Learning; Projection transformation matrix;
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