Statistical Frameworks for Mapping 3D Shape Variation onto Genotypic and Phenotypic Variation

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The recent curation of large-scale databases with 3D surface scans of shapes has motivated the development of tools that better detect global-patterns in morphological variation. Studies which focus on identifying differences between shapes have been limited to simple pairwise comparisons and rely on pre-specified landmarks (that are often known). In this talk, we present SINATRA: a statistical pipeline for analyzing collections of shapes without requiring any correspondences. Our method takes in two classes of shapes and highlights the physical features that best describe the variation between them.

See more at https://www.microsoft.com/en-us/research/video/statistical-frameworks-for-mapping-3d-shape-variation-onto-genotypic-and-phenotypic-variation/




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Tags:
Lorin Crawford
Statistical Frameworks
3D surface scans
mapping 3D shapes
genotypic variation
phenotypic variation
morphological variation
SINATRA pipeline
3D image analysis
microsoft research