A baroque approach
Combining computer vision with historical knowledge is changing the understanding of art.
Image-processing techniques, such as cast-shadow analysis, occluding-contour algorithms, and uncalibrated reconstruction of 3-D spaces, are typically associated with industrial machine-vision applications. Yet these techniques can be applied in other, very powerful ways, including analysis of images created by visual artists rather than engineers.
In the study of art history, for example, critics have long debated how masters created their pictorial perspective and some have challenged the accuracy of how the effects of light are portrayed. David Stork, chief scientist at Ricoh Innovations (Menlo Park, CA), has shown how vision algorithms can be combined with computer graphics and knowledge of art history to answer these questions. He says that in the past several decades, advances in imaging-including sophisticated cameras and imaging systems with ever increasing spatial and spectral resolution-have allowed art scholars to see into artwork as never before.
To study Magdalen with the Smoking Flame (1640) by Georges de la Tour, Stork and a colleague used an occluding-contour algorithm to compute the direction of light striking the painted surface of a young woman. The effects of the light were accurately computed by the algorithm and agreed well with the location of the light source, a candle. As a result, Stork verified the power of the algorithm and confirmed de la Tour’s mastery in rendering shapes and highlights.
In another example, Stork and a colleague built computer graphics models of Jan van Eyck’s Portrait of Giovanni Arnolfini and his wife (1434) to test how close the artist adhered to the rules of perspective. This, in turn, has shed light on the controversial claim that van Eyck secretly built an optical projector and traced images during the execution of his works.
Infrared reflectography and x-radiography have been used to reveal underdrawings-the earlier versions of some paintings. Close study of the changes an artist makes during execution of a painting show how the artist struggled to solve problems as he developed his composition. Infrared reflectograms of Portrait of Giovanni Arnolfini and his wife show how the artist adjusted the relative heights of the two figures and changed their eye contact and the positions of their feet and more throughout the development of this complex psychological study of their relationship. Each version explored a different formal composition and relationship among the figures and the symbol-laden room around them.
Perhaps the most important success of multispectral imaging in art has been the analysis of the Archimedes Palimpsest. This manuscript contained a copy of The method and Stomachion and was the only source for the Greek text On floating bodies (which dealt with specific gravity). In the 10th century, medieval scribes scraped off this text, wrote over its parchment, and rebound it as a medieval prayer book, all but obliterating Archimedes’ text. Painstaking multispectral and x-ray-fluorescence imaging together with image processing have recovered most of the original marks and enabled classics scholars to read the original text.
Imaging science has also been able to reverse the aging of artworks to restore their original color schemes. One of the greatest masterpieces of French neoimpressionism is Georges Seurat’s Un dimanche après-midi à l’Ile de Grande Jatte-1884 (1884-1886) in the Art Institute of Chicago. This large canvas was executed in the technique of pointillism-many tiny dots of color-and was deeply influenced by color theory.
To see this work as the artist intended, it is essential to see the colors as the artist applied them. Shortly after Seurat’s short life, however, critics noted that some of his pigments were fading. Recently, image scientist Roy S. Berns at the Rochester Institute of Technology (Rochester, NY) modeled mathematically the spectra and the fading of different pigments in Grande Jatte. He then ran a computer model of the fading to “un-age” the painting and produce a digital image that likely matches the painting as Seurat painted it.
In the past, image processing of art has relied heavily upon the human eye and the judgment of art scholars and connoisseurs. In the future, however, some of the analysis will be done by computer vision and pattern-recognition algorithms. To explore this topic, a symposium, “Computer image analysis in the study of art,” will be held at the SPIE/IS&T Electronic Imaging conference in San Jose, CA, on Jan. 28, 2008.
CONARD HOLTON is editor in chief of Vision Systems Design; e-mail: email@example.com.