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Principal Component Analysis applied to Real Images

Gerard Brunet (*),  Abdellah Qannari (*)


The purpose of the project is to build a fast and reliable software for recognition
of objects in aerial images. The preliminary stages consist of an edge detection by the
analysis of a color gradient vector. After edge detection and feature extraction of the
three-dimensional color histogram a set of parameters is extracted. The parameters taken
into account to recognize regions are the histograms of the R, G, and B color components
of the pixels and characteristics on the homogeneity and the shape.
Principal Component Analysis was performed for examining relationships among several
quantitative variables.The programmation language used operationally to translate is Java,
Software Development Environment was Eclipse platform with Swing components for the
Human Machine Interface. The software was applied to 400*400 pixels images. Real images
have been tested to evaluate the performance concerning translation, rotation, zoom,
and random noise addition. For reason of speed, the analysis can be made on only a part
of the image, until 1/8 of the image.

(*) University of Poitiers, Niort, France


References for the Project

Hastie, T., Tibshirani, R., Friedman, J., 2001
The Elements of Statistical Learning. Springer

Jaba, E., Qannari, A., 2013.
Analyse Discriminante avec applications sous SPSS et SAS .  Eyrolles, France.
Translated for Editura Economica, Romania.

Busin, L., Vandenbroucke, N., Macaire, L., 2007. Color spaces and image segmentation.
Internal report, UMR CNRS 8146, France.


Software Development Environment

SAS  (Statistical Analysis System)  development environment   9.3
SAS IML  Package  (interactive  Matrix  Language)
Eclipse  Platform development environment
Swing Components graphical user interface
Matlab (interactive Matrix Laboratory), for preliminary steps

principal component analysis; real images