Abstract
Ellipse center extraction is the important basis of camera calibration with circle arrays calibration board, which directly affects the measurement accuracy of machine vision system. Aiming at the problem of high noise sensitivity of the most ellipse center extraction methods, a high robust multi-ellipse center extraction method is proposed. The idea is to transform the problem of multi-ellipse center extraction into the problems of ellipse sub-pixel edge segmentation and edge based ellipse center clustering. Firstly, the flowchart of the proposed method is introduced. Then, the theory of fuzzy quotient space is introduced to establish the dynamic granularity matrix space model, which describes image segmentation problem as the jumping and transformation of the image by hierarchical structure. Finally, an adaptive multi-ellipse sub-pixel edge segmentation method and an optimal ellipse center extraction method are proposed based on the dynamic granularity matrix space model. Experimental results show that the proposed method has higher accuracy, robustness and lower camera calibration error.
Highlights
Benefiting from the advantages of non-contact and high precision, machine vision [1]–[6] has been widely used in quality detection, three-dimensional reconstruction, visual detection and other fields
The accuracy of camera calibration significantly affects the precision of stereo vision measurement system [7]–[10], accurate camera calibration needs to be studied for reducing the measurement errors and obtaining the reliable three-dimensional (3D) deformation fields of the object [10]
In order to achieve higher accuracy of ellipse center extraction and reduce the calibration error, we develop a high robust multi-ellipse center extraction (HRMCE) method based on the dynamic granularity matrix space(DGMS) model
Summary
Benefiting from the advantages of non-contact and high precision, machine vision [1]–[6] has been widely used in quality detection, three-dimensional reconstruction, visual detection and other fields. Compared with planar chessboard and Charuco board, due to the advantages of easy detection and high positioning accuracy, the circular arrays calibration board is widely used for robust camera calibration by taking the circular target centers as the matching points of binocular images. Considering the multiple ellipses detection problem, Grbić et al [28] proposed a method based on direct least squares method and the random sample consensus algorithm, which performed significantly better for ellipses with noisy edges. The region of interest (ROI) of each ellipse in calibration images is extracted by Otsu operator, and the optimal clustering of each ROI can be obtained based on the DGMS model, which is computed as the adaptive segmentation thresholds for CannyZernike operator to obtain the sub-pixel edge points of each ellipse.
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