Comparing generalized procrustes analysis and statis

Comparing Generalized Procrustes Analysis and STATIS

We consider a model for sensory profiling data including translation, rotation and scaling. We compare two methods to calculate an overall consensus from several data matrices: GPA and STATIS. These methods are briefly illustrated and explained under our model. A series of simulations to compare their performance has been carried out. We found significant differences in performance depending on...

Continuous Generalized Procrustes analysis

Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, the st...

BARTOLI, PIZARRO, LOOG: STRATIFIED GENERALIZED PROCRUSTES ANALYSIS 1 Stratified Generalized Procrustes Analysis1

This paper deals with generalized procrustes analysis. This is the problem of registering a set of shape data by finding a reference shape and global rigid transformations given point correspondences. The transformed shape data must align with the reference shape as best possible. This is a difficult problem. The classical approach computes alternatively the reference shape, usually as the aver...

Generalized Procrustes Analysis: a Tool for Exploring Aggregates and Persons

Gower (1975) introduced Generalized Procrustes Analysis (GPA) as a multivariate statistical technique for analyzing three-dimensional data matrices. The current paper presents a non-technical introduction to the logic underlying GPA and then presents a completely worked example using genuine data. Specifically, self and peer ratings obtained from students attending a Summer Science Academy are ...

Generalized Procrustes Analysis and Its Applications in Photogrammetry