NDLI: Statistical shape models of plant leaves

Content Provider	IEEE Xplore Digital Library
Author	Laga, H. Kurtek, S. Srivastava, A. Miklavcic, S.J.
Copyright Year	2013
Description	Author affiliation: Phenomics & Bioinf. Res. Centre, Univ. of South Australia, Mawson Lakes, SA, Australia (Laga, H.; Miklavcic, S.J.) \|\| Dept. of Stat., Ohio State Univ., Columbus, OH, USA (Kurtek, S.) \|\| Dept. of Stat., Florida State Univ., Tallahassee, FL, USA (Srivastava, A.)
Abstract	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.
Starting Page	3503
Ending Page	3507
File Size	206015
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738723
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Karcher mean Shape space
Content Type	Text
Resource Type	Article

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