Principal Component Analysis of Random Particles
Journal of Mathematical Imaging and Vision
Independent component analysis: algorithms and applications
Neural Networks
On Using Functions to Describe the Shape
Journal of Mathematical Imaging and Vision
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Resuming Shapes with Applications
Journal of Mathematical Imaging and Vision
Overlearning in marginal distribution-based ICA: analysis and solutions
The Journal of Machine Learning Research
Editorial: Statistics for Functional Data
Computational Statistics & Data Analysis
Classification of functional data: A segmentation approach
Computational Statistics & Data Analysis
Functional Data Analysis with R and MATLAB
Functional Data Analysis with R and MATLAB
Support vector machine for functional data classification
Neurocomputing
Dimensionality reduction when data are density functions
Computational Statistics & Data Analysis
A half-region depth for functional data
Computational Statistics & Data Analysis
Identifying cluster number for subspace projected functional data clustering
Computational Statistics & Data Analysis
Modern Applied Statistics with S
Modern Applied Statistics with S
Functional classification in Hilbert spaces
IEEE Transactions on Information Theory
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Shape description from generalized support functions
Pattern Recognition Letters
Computational Statistics & Data Analysis
Hi-index | 0.03 |
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.