In the last decade, research in Computer Vision has developed several algorithms to help botanists and non-experts to classify plants based on images of their leaves. LeafSnap is a mobile application that uses a multiscale curvature model of the leaf margin to classify leaf images into species. It has achieved high levels of accuracy on 184 tree species from Northeast US. We extend the research that led to the development of LeafSnap along two lines. First, LeafSnap's underlying algorithms are applied to a set of 66 tree species from Costa Rica. Then, texture is used as an additional criterion to measure the level of improvement achieved in the automatic identification of Costa Rica tree species. A 25.6% improvement was achieved for a Costa Rican clean image dataset and 42.5% for a Costa Rican noisy image dataset. In both cases, our results show this increment as statistically significant. Further statistical analysis of visual noise impact, best algorithm combinations per species, and best value of k, the minimal cardinality of the set of candidate species that the tested algorithms render as best matches is also presented in this research.

Abstract Recent research on point-based surface representations suggests that point sets may be a viable alternative to parametric surface representations in applications where the topological constraints of a parameterization are unwieldy or inefficient. Particle systems offer a mechanism for controlling point samples and distributing them according to needs of the application.