Journal
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
Volume 10, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fbioe.2022.955233
Keywords
fisheye camera; stereo calibration; phase unwrapping; neural-network; large field of view
Funding
- Open Fund of the Key Laboratory for Metallurgical Equipment and Control of Ministry of Education in Wuhan University of Science and Technology [MECOF2021B03]
- Natural Science Foundation of Hubei Province [2020CFB549]
- Open Fund of Key Laboratory of Icing and Anti/Deicing [IADL20200308]
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This paper presents a model-free stereo calibration method for binocular fisheye cameras based on neural networks, which can effectively solve the problem of barrel distortion and preserve the advantages of the wide field of view of fisheye cameras.
The fisheye camera has a field of view (FOV) of over 180 degrees, which has advantages in the fields of medicine and precision measurement. Ordinary pinhole models have difficulty in fitting the severe barrel distortion of the fisheye camera. Therefore, it is necessary to apply a nonlinear geometric model to model this distortion in measurement applications, while the process is computationally complex. To solve the problem, this paper proposes a model-free stereo calibration method for binocular fisheye camera based on neural-network. The neural-network can implicitly describe the nonlinear mapping relationship between image and spatial coordinates in the scene. We use a feature extraction method based on three-step phase-shift method. Compared with the conventional stereo calibration of fisheye cameras, our method does not require image correction and matching. The spatial coordinates of the points in the common field of view of binocular fisheye camera can all be calculated by the generalized fitting capability of the neural-network. Our method preserves the advantage of the broad field of view of the fisheye camera. The experimental results show that our method is more suitable for fisheye cameras with significant distortion.
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