Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

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.