Fig. 2. ANN for learning the projection of a camera.
Fig. 3. Proposed neural camera calibration method.
Fig. 4. Calibration points are marked by ‘+’. They are distorted mainly by the lens and the degree of distortion is represented by the circle size.
Fig. 5. Error circles by the proposed method for the calibration data.
Fig. 1. Pinhole camera model.
Table 1. Camera parameters assumed for test.
Table 2. Lens distortion and noise parameters.
Table 3. Performance(projection error in [mm]) for different number of hidden nodes
Table 4. Comparative test results [mm]
Table 5. Test results using more learning data: Twenty data are used here for calibration while ten data are used in the test of Table 4.
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