Data fusion in the wavelet domain by means of fuzzy aggregation connectives.

Dual-domain classification analysis is proposed to identify pigments used in works of art studied by Raman spectroscopy and X-ray fluorescence spectrometry. By means of this methodology, Raman and X-ray fluorescence data are jointly processed by a high-level fusion approach. The system proposed aims to avoid the pre-processing stage and directly process raw data obtained from the instrument. The system is tested with spectra contaminated with background components of different shapes and intensities and with those with the background removed by line segment correction. The benefits of the approach were well demonstrated in a study of an ochre pigment classification. The approach is based on the main advantage of wavelet transform, which is multiresolution. Each spectrum is split into blocks, according to a specific frequency, to form a wavelet prism. Partial least squares-discriminant analysis (PLS-DA) is then applied to those blocks which contain the deterministic part of the signal and are not influenced by noise and background signal components. At the end, to obtain the final classification assignment, high-level data fusion of the classifications results (decision levels) obtained from PLS-DA analysis is done by means of fuzzy aggregation connective operators. Our study showed that fuzzy aggregation may be suitable for performing high-level data fusion on dual-domain data. This method can be automated so that classification can be rapid. It can handle classifications with different levels of difficulty and requires no prior knowledge of sample composition.

[1]  Koen Janssens,et al.  Non-destructive Micro Analysis of Cultural Heritage Materials , 2004 .

[2]  Pascal Boilot,et al.  Electronic noses inter-comparison, data fusion and sensor selection in discrimination of standard fruit solutions , 2003 .

[3]  V. Steinmetz,et al.  Sensors for fruit firmness assessment : Comparison and fusion , 1996 .

[4]  P Vandenabeele,et al.  A new instrument adapted to in situ Raman analysis of objects of art , 2004, Analytical and bioanalytical chemistry.

[5]  Dalva Lúcia Araújo de Faria,et al.  Raman microspectroscopy of some iron oxides and oxyhydroxides , 1997 .

[6]  H. Edwards,et al.  Caput mortuum: spectroscopic and structural studies of an ancient pigment. , 2002, The Analyst.

[7]  P. M. Ramos,et al.  Data fusion and dual-domain classification analysis of pigments studied in works of art , 2006 .

[8]  Andrew Starr,et al.  A Review of data fusion models and architectures: towards engineering guidelines , 2005, Neural Computing & Applications.

[9]  Ching Y. Suen,et al.  Application of majority voting to pattern recognition: an analysis of its behavior and performance , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[10]  Yang Liu,et al.  Wavelet multiscale regression from the perspective of data fusion: new conceptual approaches , 2004, Analytical and bioanalytical chemistry.

[11]  K. Andrikopoulos,et al.  In vitro validation of a mobile Raman–XRF micro‐analytical instrument's capabilities on the diagnosis of Byzantine icons , 2006 .

[12]  E. Pavlidou,et al.  Ochre-differentiation through micro-Raman and micro-FTIR spectroscopies: application on wall paintings at Meteora and Mount Athos, Greece. , 2000, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[13]  P. Vandenabeele,et al.  Spectroscopic Examination of Two Egyptian Masks: A Combined Method Approach , 2000 .

[14]  Z. S. Xu,et al.  An overview of operators for aggregating information , 2003, Int. J. Intell. Syst..

[15]  V. Steinmetz,et al.  A Methodology for Sensor Fusion Design: Application to Fruit Quality Assessment , 1999 .

[16]  J. Roger,et al.  Fusion of aroma, FT-IR and UV sensor data based on the Bayesian inference. Application to the discrimination of white grape varieties , 2003 .

[17]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[18]  Beata Walczak,et al.  Wavelets in Chemistry , 2001 .

[19]  Steven D. Brown,et al.  Multivariate calibration of spectral data using dual-domain regression analysis , 2003 .

[20]  Didier Dubois,et al.  A review of fuzzy set aggregation connectives , 1985, Inf. Sci..

[21]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Steven D. Brown,et al.  Dual‐domain regression analysis for spectral calibration models , 2003 .

[23]  Steven D. Brown,et al.  Wavelet analysis applied to removing non‐constant, varying spectroscopic background in multivariate calibration , 2002 .

[24]  Véronique Bellon-Maurel,et al.  Authenticating white grape must variety with classification models based on aroma sensors, FT-IR and UV spectrometry , 2003 .

[25]  P. M. Ramos,et al.  Noise and background removal in Raman spectra of ancient pigments using wavelet transform , 2005 .

[26]  P. Moioli,et al.  Analysis of art objects using a portable X-ray fluorescence spectrometer , 2000 .

[27]  Isabelle Bloch.  Information combination operators for data fusion: a comparative review with classification , 1996, IEEE Trans. Syst. Man Cybern. Part A.