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High precision camera calibration

Abstract : The thesis focuses on precision aspects of 3D reconstruction with a particular emphasis on camera distortion correction. The causes of imprecisions in stereoscopy can be found at any step of the chain. The imprecision caused in a certain step will make useless the precision gained in the previous steps, then be propagated, amplified or mixed with errors in the following steps, finally leading to an imprecise 3D reconstruction. It seems impossible to directly improve the overall precision of a reconstruction chain leading to final imprecise 3D data. The appropriate approach to obtain a precise 3D model is to study the precision of every component. A maximal attention is paid to the camera calibration for three reasons. First, it is often the first component in the chain. Second, it is by itself already a complicated system containing many unknown parameters. Third, the intrinsic parameters of a camera only need to be calibrated once, depending on the camera configuration (and at constant temperature). The camera calibration problem is supposed to have been solved since years. Nevertheless, calibration methods and models that were valid for past precision requirements are becoming unsatisfying for new digital cameras permitting a higher precision. In our experiments, we regularly observed that current global camera methods can leave behind a residual distortion error as big as one pixel, which can lead to distorted reconstructed scenes. We propose two methods in the thesis to correct the distortion with a far higher precision. With an objective evaluation tool, it will be shown that the finally achievable correction precision is about 0.02 pixels. This value measures the average deviation of an observed straight line crossing the image domain from its perfectly straight regression line. High precision is also needed or desired for other image processing tasks crucial in 3D, like image registration. In contrast to the advance in the invariance of feature detectors, the matching precision has not been studied carefully. We analyze the SIFT method (Scale-invariant feature transform) and evaluate its matching precision. It will be shown that by some simple modifications in the SIFT scale space, the matching precision can be improved to be about 0.05 pixels on synthetic tests. A more realistic algorithm is also proposed to increase the registration precision for two real images when it is assumed that their transformation is locally smooth. A multiple-image denoising method, called ''burst denoising'', is proposed to take advantage of precise image registration to estimate and remove the noise at the same time. This method produces an accurate noise curve, which can be used to guide the denoising by the simple averaging and classic block matching method. ''burst denoising'' is particularly powerful to recover fine non-periodic textured part in images, even compared to the best state of the art denoising method.
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Submitted on : Thursday, March 1, 2012 - 11:21:37 AM
Last modification on : Thursday, July 2, 2020 - 5:17:18 PM
Long-term archiving on: : Thursday, May 31, 2012 - 2:36:41 AM


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  • HAL Id : tel-00675484, version 1



Zhongwei Tang. High precision camera calibration. General Mathematics [math.GM]. École normale supérieure de Cachan - ENS Cachan, 2011. English. ⟨NNT : 2011DENS0024⟩. ⟨tel-00675484⟩



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