Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics
Fig 3
Improving correspondence with Consistent ZoomOut.
We compare maps generated by HSN ResUNet descriptor learning model to their Consistent ZoomOut-refined counterparts for four randomly chosen pairs of hominoid cuboids. Rows 3A.1 and 3A.2 show source and target shape pairs produced by our model, respectively. Rows 3B.1 and 3B.2 show the same source and target shape pairs after Consistent ZoomOut refinement. Between shape pairs, surface regions of the same color (green/purple/pink/yellow) are considered homologous. Black areas on source shapes indicate a lack of bijective coverage with its associated location on the corresponding target.