Robust automated multiple view inspection

Abstract

Recently, Automated Multiple View Inspection (AMVI) has been developed for automated defect detection of manufactured objects. That framework was successfully implemented for calibrated image sequences. However, it is not easy to implement in industrial environments because the calibration is a difficult and unstable process. To overcome these disadvantages, we propose the robust AMVI strategy which assumes that an unknown affine transformation exists between each pair of uncalibrated images. This transformation is estimated using two complementary robust procedures: a global approximation of the affine mapping is computed by creating candidate correspondences via B-splines and selecting those which better satisfy the epipolar constraint for uncalibrated images. Then, we use this approximation as initial estimate of a robust intensity-based matching approach, which is applied locally on each potential defect. The result that false alarms are discarded, and the defects of an industrial object are actually tracked along the uncalibrated image sequence. The method is successful as shown in our experiments on aluminum die castings.