In 2001, artist David Hockney and scientist Charles Falco stunned the art world with a controversial theory that, if correct, would profoundly alter our view of the development of image making. They claimed that as early as 1420, Renaissance artists employed optical devices such as concave mirrors to project images onto their canvases, which they then traced or painted over. In this way, the theory attempts to explain the newfound heightened naturalism or “opticality” of painters such as Jan van Eyck, Robert Campin, Hans Holbein the Younger, and many others.
This talk will describe the application of rigorous computer image analysis to masterpieces adduced as evidence for this theory. It covers basic geometrical optics of image projection, the analysis of perspective, curved surface reflections, shadows, lighting and color. While there remain some loose ends, such analysis of the paintings, infra-red reflectograms, modern reenactments, internal consistency of the theory, and alternate explanations allows us to judge with high confidence the plausibility of this bold theory. You may never see Renaissance paintings the same way again.
Joint work with Antonio Criminisi, Marco Duarte, M. Kimo Johnson and Christopher W. Tyler.
Dr. David G. Stork
Ph.D., Ricoh Innovations, Inc.
David G. Stork, Chief Scientist of Ricoh Innovations, has held consulting or visiting faculty positions at Stanford University in Computer Science, Statistics, Electrical Engineering, Psychology and Art and Art History starting in 1989. He studied art history at Wellesley College and was Artist-in-Residence through the New York State Council of the Arts. He holds 35 patents and has published over 140 scholarly articles and five books, including Seeing the Light: Optics in Nature, Photography, Color, Vision and Holography with D. Falk and D. Brill and Pattern Classification (2nd ed.) with R. Duda and P. Hart. His current research interests center on the joint design of optical and image processing systems, statistical theory of data acquisition, and computer vision applied to master paintings.