Intrinsic inference on the mean geodesic of planar shapes and tree discrimination by leaf growth
平面形状平均测地线的内蕴推断与基于叶片生长的树木区分AI 解读 ↓
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平面形状平均测地线的内蕴推断与基于叶片生长的树木区分AI 解读 ↓
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For planar landmark based shapes, taking into account the non-Euclidean geometry of the shape space, a statistical test for a common mean first geodesic principal component (GPC) is devised which rests on one of two asymptotic scenarios. For both scenarios, strong consistency and central limit theorems are established, along with an algorithm for the computation of a Ziezold mean geodesic. In application, this allows to verify the geodesic hypothesis for leaf growth of Canadian black poplars and to discriminate genetically different trees by observations of leaf shape growth over brief time intervals. With a test based on Procrustes tangent space coordinates, not involving the shape space’s curvature, neither can be achieved.