STATISTICAL SHAPE MODELS OF PLANT LEAVES

The shapes of plant leaves are of great importance to plant biologists and botanists, as they can help in distinguishing plant species, measuring their health, analyzing their growth patterns, and understanding relations between various species. We propose a statistical model that uses the Squared Root Velocity Function representation and a Riemannian elastic metric to model the observed variability in the shape of plant leaves. We show that under this representation, one can compute sample means and principal modes of variations and can characterize the observed shapes using probability models, such as Gaussians, on the tangent spaces at the sample means. The approach is fully automatic and does not require precomputing correspondences between the shapes. We validate these statistical models by analyzing their classification performance on standard benchmarks and show their utility as generative models for random sampling.