Epifanio, Irene; Ventura-Campos, Noelia Functional data analysis in shape analysis. (English) Zbl 1464.62064 Comput. Stat. Data Anal. 55, No. 9, 2758-2773 (2011). Summary: Mid-level processes on images often return outputs in functional form. In this context the use of functional data analysis (FDA) in image analysis is considered. In particular, attention is focussed on shape analysis, where the use of FDA in the functional approach (contour functions) shows its superiority over other approaches, such as the landmark based approach or the set theory approach, on two different problems (principal component analysis and discriminant analysis) in a well-known database of bone outlines. Furthermore, a problem that has hardly ever been considered in the literature is dealt with: multivariate functional discrimination. A discriminant function based on independent component analysis for indicating where the differences between groups are and what their level of discrimination is, is proposed. The classification results obtained with the methodology are very promising. Finally, an analysis of hippocampal differences in Alzheimer’s disease is carried out. Cited in 5 Documents MSC: 62-08 Computational methods for problems pertaining to statistics 62H25 Factor analysis and principal components; correspondence analysis 62H30 Classification and discrimination; cluster analysis (statistical aspects) 62R10 Functional data analysis Keywords:form analysis; multivariate functional data analysis; curve classification; shape discrimination; principal component analysis; outlines Software:mda; FastICA; R; MASS (R); S-PLUS; Rainbow; ElemStatLearn; fda (R) PDFBibTeX XMLCite \textit{I. Epifanio} and \textit{N. Ventura-Campos}, Comput. Stat. 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