the blocking of machine cover and tool holder exists). Whereas the traditional method that needs camera calibration is mostly used in large field of view
which is not suitable for on-site tool wear detection. An uncalibrated method based on binocular vision is proposed in this paper for obtaining the disparity map of tool wear images without calibration reference or man-induced intervention. Firstly
SURF(speeded-up robust features)algorithm is used to detect the images' feature points. Then
the 8-point algorithm is adopted to estimate the fundamental matrix; next
image correction is completed by pole infinity transform. Finally
the disparity map is obtained by basic block matching algorithm. On-site experiments were conducted to obtain the tool wear images
and the analysis on profile and accuracy of the reconstructed disparity map was performed. The results show that the reconstructed profile is basically consistent with the actual profile; the absolute error of the reconstructed disparity map is less than 5 pixels and the relative error is between 10% and 30%
meaning that the disparity map obtained by the uncalibrated method in small field of view and complex site condition can meet the accuracy requirement for on-site detection
and this method is also flexible and effective.
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references
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