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Everything about Statistical shape analysis

Analysis of geometric properties

Statistical shape analysis is an analysis of the geometrical properties of some given set of shapes by statistical methods. For instance, it could be used to quantify differences between male and female gorilla skull shapes, normal and pathological bone shapes, leaf outlines with and without herbivory by insects, etc. Important aspects of shape analysis are to obtain a measure of distance between shapes, to estimate mean shapes from samples, to estimate shape variability within samples, to perform clustering and to test for differences between shapes. One of the main methods used is principal component analysis (PCA). Statistical shape analysis has applications in various fields, including medical imaging, computer vision, computational anatomy, sensor measurement, and geographical profiling. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Statistical shape analysisA 5-minute orientation
  2. Skim a recent paper: Statistical Analysis of H i Profile Asymmetry and Shape for Nearby GalaxiesInfluential work since 2022
  3. Pick up a classic book: Free-form airfoil shape optimization under uncertainty using maximum expected value and second-order second-moment strategiesThe most-reprinted book on the subject
  4. Read the landmark paper: Procrustes Methods in the Statistical Analysis of ShapeCited 1,652 times
  5. Go sideways: DiffeomorphometryA closely related topic
  6. Go sideways: MorphometricsA closely related topic

📖 Definitions

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🗂️ The subject at a glance

  1. Landmark-based techniques
  2. Shape deformations

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

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🔗 Related topics