scholarly journals Segmentation of multispectral images of works of art through principal component analysis

1997 ◽  
pp. 14-21 ◽  
Author(s):  
Stefano Baronti ◽  
Andrea Casini ◽  
Franco Lotti ◽  
Simone Porcinai
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multispectral Images ◽  
Works Of Art

Principal Component Analysis of Spectral Data: A Contribution to the Knowledge of the Materials Constituting Works of Art

1996 ◽  
Vol 462 ◽  
Author(s):  
M. Bacci ◽  
S. Baronti ◽  
A. Casini ◽  
F. Lotti ◽  
M. Picollo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Spectral Data ◽  
Laboratory Tests ◽  
Principal Component ◽  
Component Analysis ◽  
Work Of Art ◽  
Works Of Art ◽  
Image Spectroscopy ◽  
Non Destructive ◽  
Non Destructive Techniques

ABSTRACTThe use of totally non-destructive techniques such as image spectroscopy for diagnosing paintings makes it possible to obtain a large amount of spectral data that provides information concerning the composition of works of art. Here, we stress how statistical treatments, such as principal component analysis (PCA), applied to 2-D data, can contribute to a better knowledge of the work of art itself and of the distribution of the materials that constitute it.Laboratory tests, as well as applications to actual paintings, will be presented and discussed.

Sign in / Sign up

Export Citation Format

Share Document