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| Statistical Shape Model ： ウィキペディア英語版 |
Statistical shape analysis
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, etc. Important aspects of shape analysis are to obtain a measure of distance between shapes, to estimate mean shapes from (possibly random) 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. Statistical shape analysis has applications in various fields, including medical imaging, computer vision, sensor measurement, and geographical profiling.
== Modeling ==
The first step after collecting a set of shapes is to create a proper shape model for further statistical analysis. In the point distribution model, a shape is determined by a finite set of coordinate points, known as landmark points; the Cartesian coordinate system is the most commonly used one. Alternatively, shapes can be represented by curves or surfaces representing their contours, by the spacial region they occupy, etc.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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