S. Hughes and I. Daubechies :
“Simpler alternatives to information theoretic similarity metrics for multimodal image alignment 2006 International conference on image processing
(Atlanta, GA, 8–11 October 2006 ).
Proceedings, International Conference on Image Processing .
IEEE (Piscataway, NJ ),
2007 .
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Mutual information (MI) based methods for image registration enjoy great experimental success and are becoming widely used. However, they impose a large computational burden that limits their use; many applications would benefit from a reduction of the computational load. Although the theoretical justification for these methods draws upon the stochastic concept of mutual information, in practice, such methods actually seek the best alignment by maximizing a number that is (deterministically) computed from the two images. These methods thus optimize a fixed function, the “similarity metric,” over different candidate alignments of the two images. Accordingly, we study the important features of the computationally complex MI similarity metric with the goal of distilling them into simpler surrogate functions that are easier to compute. More precisely, we show that maximizing the MI similarity metric is equivalent to minimizing a certain distance metric between equivalence classes of images, where images \( f \) and \( g \) are said to be equivalent if there exists a bijection \( \phi \) such that \( f(x) = \phi(g(x)) \) for all \( x \) . We then show how to design new similarity metrics for image alignment with this property. Although we preserve only this aspect of MI, our new metrics show equal alignment accuracy and similar robustness to noise, while significantly decreasing computation time. We conclude that even the few properties of MI preserved by our method suffice for accurate registration and may in fact be responsible for MI’s success.
@incollection {key40044866,
AUTHOR = {Hughes, S. and Daubechies, I.},
TITLE = {Simpler alternatives to information
theoretic similarity metrics for multimodal
image alignment},
BOOKTITLE = {2006 {I}nternational conference on image
processing},
SERIES = {Proceedings, International Conference
on Image Processing},
PUBLISHER = {IEEE},
ADDRESS = {Piscataway, NJ},
YEAR = {2007},
DOI = {10.1109/ICIP.2006.313169},
NOTE = {(Atlanta, GA, 8--11 October 2006).},
ISSN = {1522-4880},
}
C. R. Johnson, Jr., E. Hendriks, I. J. Berezhnoy, E. Brevdo, S. M. Hughes, I. Daubechies, J. Li, E. Postma, and J. Z. Wang :
“Image processing for artist identification: Computerized analysis of Vincent van Gogh’s painting brushstrokes IEEE Signal Process. Mag.
25 : 4
(2008 ),
pp. 37–48 .
article
Abstract
People
BibTeX
A survey of the literature reveals that image processing tools aimed at supplementing the art historian’s toolbox are currently in the earliest stages of development. To jump-start the development of such methods, the Van Gogh and Kroller–Muller museums in The Netherlands agreed to make a data set of 101 high-resolution gray-scale scans of paintings within their collections available to groups of image processing researchers from several different universities. This article describes the approaches to brushwork analysis and artist identification developed by three research groups, within the framework of this data set.
@article {key85720119,
AUTHOR = {Johnson, Jr., C. Richard and Hendriks,
E. and Berezhnoy, I. J. and Brevdo,
E. and Hughes, S. M. and Daubechies,
I. and Li, J. and Postma, E. and Wang,
J. Z.},
TITLE = {Image processing for artist identification:
{C}omputerized analysis of {V}incent
van {G}ogh's painting brushstrokes},
JOURNAL = {IEEE Signal Process. Mag.},
FJOURNAL = {IEEE Signal Processing Magazine},
VOLUME = {25},
NUMBER = {4},
YEAR = {2008},
PAGES = {37--48},
DOI = {10.1109/MSP.2008.923513},
ISSN = {1053-5888},
}
S. Jafarpour, G. Polatkan, E. Brevdo, S. Hughes, A. Brasoveanu, and I. Daubechies :
“Stylistic analysis of paintings using wavelets and machine learning 1220–1224
in
17th European signal processing conference (EUSIPCO 2009)
(Glasgow, Scotland, 24–28 August 2009 ).
IEEE (Piscataway, NJ ),
2009 .
incollection
Abstract
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Wavelet transforms and machine learning tools can be used to assist art experts in the stylistic analysis of paintings. A dual-tree complex wavelet transform, Hidden Markov Tree modeling and Random Forest classifiers are used here for a stylistic analysis of Vincent van Gogh’s paintings with results on two stylometry challenges that concern “dating, resp. extracting distinguishing features”.
@incollection {key46171783,
AUTHOR = {Jafarpour, S. and Polatkan, G. and Brevdo,
E. and Hughes, S. and Brasoveanu, A.
and Daubechies, I.},
TITLE = {Stylistic analysis of paintings using
wavelets and machine learning},
BOOKTITLE = {17th {E}uropean signal processing conference
({EUSIPCO} 2009)},
PUBLISHER = {IEEE},
ADDRESS = {Piscataway, NJ},
YEAR = {2009},
PAGES = {1220--1224},
URL = {http://ieeexplore.ieee.org/abstract/document/7077807/},
NOTE = {(Glasgow, Scotland, 24--28 August 2009).},
ISBN = {9781617388767},
}
G. Polatkan, S. Jafarpour, A. Brasoveanu, S. Hughes, and I. Daubechies :
“Detection of forgery in paintings using supervised learning 2921–2924
in
2009 IEEE international conference on image processing
(Cairo, 7–12 November 2009 ).
Proceedings, International Conference on Image Processing .
IEEE (Piscataway, NJ ),
2009 .
incollection
Abstract
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This paper examines whether machine learning and image analysis tools can be used to assist art experts in the authentication of unknown or disputed paintings. Recent work on this topic has presented some promising initial results. Our reexamination of some of these recently successful experiments shows that variations in image clarity in the experimental datasets were correlated with authenticity, and may have acted as a confounding factor, artificially improving the results. To determine the extent of this factor’s influence on previous results, we provide a new “ground truth” data set in which originals and copies are known and image acquisition conditions are uniform. Multiple previously-successful methods are found ineffective on this new confounding-factor-free dataset, but we demonstrate that supervised machine learning on features derived from hidden-Markov-tree-modeling of the paintings’ wavelet coefficients has the potential to distinguish copies from originals in the new dataset.
@incollection {key35650542,
AUTHOR = {Polatkan, G. and Jafarpour, S. and Brasoveanu,
A. and Hughes, S. and Daubechies, I.},
TITLE = {Detection of forgery in paintings using
supervised learning},
BOOKTITLE = {2009 {IEEE} international conference
on image processing},
SERIES = {Proceedings, International Conference
on Image Processing},
PUBLISHER = {IEEE},
ADDRESS = {Piscataway, NJ},
YEAR = {2009},
PAGES = {2921--2924},
DOI = {10.1109/ICIP.2009.5413338},
NOTE = {(Cairo, 7--12 November 2009).},
ISSN = {1522-4880},
ISBN = {9781424456543},
}
A. Anitha, A. Brasoveanu, M. F. Duarte, S. M. Hughes, I. Daubechies, J. Dik, K. Janssens, and M. Alfeld :
“Virtual underpainting reconstruction from X-ray fluorescence imaging data 1239–1243
in
2011 19th European signal processing conference
(Barcelona, 29 August–2 September 2011 ).
IEEE (Piscataway, NJ ),
2011 .
incollection
Abstract
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This paper describes our work on the problem of reconstructing the original visual appearance of underpaintings (paintings that have been painted over and are now covered by a new surface painting) from noninvasive X-ray fluorescence imaging data of their canvases. This recently-developed imaging technique yields data revealing the concentrations of various chemical elements at each spatial location across the canvas. These concentrations in turn result from pigments present in both the surface painting and the underpainting beneath. Reconstructing a visual image of the underpainting from this data involves repairing acquisition artifacts in the dataset, underdetermined source separation into surface and underpainting features, identification and inpainting of areas of information loss, and finally estimation of the original paint colors from the chemical element data. We will describe methods we have developed to address each of these stages of underpainting recovery and show results on lost underpaintings.
@incollection {key10544872,
AUTHOR = {Anitha, Anila and Brasoveanu, Andrei
and Duarte, Marco F. and Hughes, Shannon
M. and Daubechies, Ingrid and Dik, Joris
and Janssens, Koen and Alfeld, Matthias},
TITLE = {Virtual underpainting reconstruction
from {X}-ray fluorescence imaging data},
BOOKTITLE = {2011 19th {E}uropean signal processing
conference},
PUBLISHER = {IEEE},
ADDRESS = {Piscataway, NJ},
YEAR = {2011},
PAGES = {1239--1243},
URL = {https://ieeexplore.ieee.org/document/7074289},
NOTE = {(Barcelona, 29 August--2 September 2011).},
ISSN = {2076-1465},
}
