Eliminating False Positives During Corner Finding By Merging Similar Segments

Aaron Wolin, Brandon Paulson, and Tracy Hammond

Summary

The authors present MergeCF, a corner finding algorithm that computes an initial set of corners and then removes false positives by merging line segments. The initial corners are determined by calculating the curvature and speed at each point, and any point whose curvature value is above a specific threshold and speed value is below is deemed a corner. Small line segments are then merged with that adjacent line segment that causes the least primitive fit error. The algorithm reported a higher accuracy for both correct corners found and "all or nothing" versus algorithms by Sezgin and Kim. MergeCF saw a significant accuracy increase in "all or nothing."

Discussion

This approach of removing false positives is important in obtaining high "all or nothing" accuracies. Wolin et al. have a simple and quick approach. Further work could be done to analyze what false positives are not getting removed, and adding functionality to the algorithm to remove these false positives, thereby increasing "all or nothing" accuracy.