초록

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Abstract ▼ AI-Helper

Outliers cause the increase of error of the transformation matrix in the pattern-matching process. To improve the accuracy, outliers must be removed. RANSAC is frequently used to remove outliers. However, a few outliers would still be remained after the outlier removal process. If these remained out...

Outliers cause the increase of error of the transformation matrix in the pattern-matching process. To improve the accuracy, outliers must be removed. RANSAC is frequently used to remove outliers. However, a few outliers would still be remained after the outlier removal process. If these remained outliers can also be removed, the accuracy will further be increased. This article proposes a new outlier removal method to improve the accuracy of markerless tracking using residual magnitude. The computation time should be fast enough to be used in real-time interactive applications. We implemented the outlier removal algorithm and then performed experiments on real images captured from a PC camera. We compared the accuracy and the execution time and found the matching accuracy was increased without increasing further time cost.

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