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An improved checkerboard detection algorithm based on adaptive filters
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DOI：10.1016/j.patrec.2023.05.032.png)
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Abstract
En 中文
Checkerboard corner extraction is a crucial step in camera calibration. However, most existing algorithms are not good enough if the lens distortion is too large. This study aims to propose a checkerboard corner detection algorithm based on adaptive filters to address this problem. First, adaptive filters based on local image features are used to generate response maps of checkerboard images. Next, a non-max suppres-sion and a scoring system are applied for further screening. Finally, the whole checkerboard structure is restored via inertia growth. The algorithm proposed is subject to rigorous experimental validation using synthetic and real images. Compared with several state of the art methods, our algorithm has the best performance.& COPY; 2023 Elsevier B.V. All rights reserved.
Keywords:
Checkerboard detection
Corner detection
Camera calibration
Lens distortion
Adaptive filters
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