Calibration pattern of camera and calibration and detection method based on same

Abstract

The invention discloses a calibration pattern of a camera and a calibration and detection method based on the same. In the invention, the center of a standard circle is taken as a characteristic point of a calibration pattern; and an equivalent center at a specific region is calculated, thus the algorithm is simple, the noise resistant property is strong, and even though an image is distorted (e.g. a circle is distorted into an ellipse), the centroid position of the image can also be correctly extracted. In addition, in the calibrating process, the object capable of being detected is arranged in a rectangular region delineated by calibrating circles and simultaneously the calibration and detection are completed, thereby reducing the influence of environment variation on the accuracy of detection. The surface of the calibration pattern is provided with eight calibrating circles, and is provided with a rectangular region, and the surface degree of the calibration pattern is different from the circular degree of the calibrating circles.

Description

technical field [0001] The invention relates to the field of camera vision, in particular to a camera calibration target and a calibration detection method based on the calibration target. Background technique [0002] The electronic tag 3D computer vision system should be able to calculate the geometric information such as the position and shape of the 3D environmental objects from the image information captured by the camera, and thus recognize the objects in the environment. The brightness of each point on the image reflects the intensity of reflected light at a point on the surface of the space object, and the position of the point on the image is related to the geometric position of the corresponding point on the surface of the space object. The relationship between these positions is determined by the geometric model of the camera imaging. Determined, the parameters of the geometric model are called camera parameters, these parameters must be determined by experiments ...

AI Technical Summary

Problems solved by technology

For example, the calibration method based on the intersection of black and white squares on the chessboard as the feature point of the calibration object is often obtained by corner point extraction methods such as Harris and Susan. Although this method has high precision, it has weak anti-noise performance and a large amount of calculation.

Moreover, using this method, the calibration The detection cannot be carried out at the same time. After the calibration is completed, changes in the environment (such as changes in light or movement of the camera, etc.) will affect the accuracy of subsequent detection

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