Robust point matching (RPM) simultaneously estimates correspondences and non-rigid warps between unstructured point-sets. While RPM is robust to outliers in the target (fixed) point-set, its performance degrades when the template (moving) point-set contains outliers. Additionally, RPM does not utilize information about the topological structure or group memberships of the data it is matching. Bi-directional Labeled Point Matching (BLPM) extends the RPM objective function by introducing (i) Bi-Directionality (BD) and (ii) a Label Entropy (LE) term. BD aids in outlier rejection in both point-sets and LE discourages mappings that transform points within a single group in one point-set onto points from multiple distinct groups in the other point-set. The resulting BLPM algorithm translates into simple modifications to the standard RPM update rules.
['Roshni Bhagalia', 'James V. Miller', 'Arunabha S. Roy']
Bi-directional labeled point matching