An improved checkerboard detection algorithm based on adaptive filters

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 f...

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Bibliographic Details
Published inPattern recognition letters Vol. 172; pp. 22 - 28
Main Authors Sang, Qiang, Huang, Tao, Wang, Hongyi
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.08.2023
Subjects
Adaptive filters
Camera calibration
Checkerboard detection
Corner detection
Lens distortion
Camera calibration
Adaptive filters
41A10
65D05
Lens distortion
65D17
41A05
Corner detection
Checkerboard detection
Online AccessGet full text
ISSN0167-8655
1872-7344
DOI10.1016/j.patrec.2023.05.032

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Summary: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 suppression 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.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2023.05.032