A calibration trilogy of monocular-vision-based aircraft boresight system

Ye, Nan; Yang, Bowen; Zhou, Hance; Zhang, Liyan

doi:10.1016/j.measurement.2017.12.011

Cited by 11 publications

(1 citation statement)

References 23 publications

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“…The image processing system uses VS2013+OpenCV3.0 and MATLAB graphics modules. Measuring the lens distortion parameters at different positions before each experiment, it can be seen that most of the parameters can negligible according to the accuracy requirements of the experiment [43,44] . Suppose the geometric coordinates of point (x, y) on the image plane under the camera coordinate system are ε x (x, y) and ε y (x, y).…”

Section: Otsu Threshold Segmentation Effect and Adaboost Classificatimentioning

confidence: 99%

Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature AdaBoost classifier

Wang

Wei-liang

et al. 2020

International Journal of Agricultural and Biological Engineering

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Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines. This study proposed a three-dimensional (3D) recognition method combining monocular vision and machine learning algorithms. According to the color characteristics of the targets, to convert the original color picture into YC b C r mode and use the 2D Otsu algorithm to perform gray level image segmentation on the C b channel. Then the Haar-feature training was carried out. The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms, while its false recognized rate was 3.91%. The strong classifier was formed by weight combination, and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system. The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck. It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature. The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm. Its correct rate can reach 95%. It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.

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Section: Otsu Threshold Segmentation Effect and Adaboost Classificatimentioning

confidence: 99%