This course is an introduction to the application of computer vision and image analysis to problems in art and art history, specifically realist art. Realist paintings are a rich source of information, both of the scene portrayed and the techniques the artist used to render that scene. Students will learn the principles of perspective and how to apply perspective analysis to paintings to infer vanishing points, locate perspective inconsistencies and to determine whether the artist used perspective constructions or tools. Students will learn how to infer the number, color, and position of light sources based on position, color and blur of cast shadows and highlights along occluding boundaries. Students will learn how to estimate sizes of depicted objects based on perspective and fiducial or reference objects or relationships. Students will learn how to estimate "camera parameters" of the artist (or imaging system), such as the effective magnification, focal length and in some cases aberrations. Some of these methods require no more than ruler and pencil, others require commercial software (e.g., Adobe Illustrator), others were adapted from their use in forensic analysis of digital photographs and require powerful commercial image processing packages (including ones based on C++, Matlab, Mathematica), and yet others require researchers to write special code.
Image processing and computer vision researchers interested in rigorous image analysis and art, as well as conservators and curators seeking new analytical techniques. Prior knowledge of basic art history and media to the level of a college survey course is desirable, but will not be assumed. Familiarity with commercial image software such as Adobe Photoshop is essential; knowledge of image processing and computer vision packages and languages such as Matlab, Mathematica and C++ is desirable, but not essential.
Introductory
David G. Stork is Rambus Fellow and leads research in the Computational Sensing and Imaging Group within Rambus Labs. He has published eight books or proceedings volumes, including Pattern Classification (2nd ed), Seeing the Light, HAL's Legacy, Computer image analysis in the study of art and Computer vision and image analysis of art. He and his colleagues have pioneered the application of rigorous computer image analysis in the study of art and he has lectured in eighteen countries on the topic, including many major art museums worldwide. He is a Fellow and Life Member of SPIE and of the International Association of Pattern Recognition (IAPR) "...for applications of computer vision to the study of art," and of IARIA, and a member of IEEE (Senior), ACM (Senior Life), and OSA (Senior Life), co-chair of SPIE's 2008, 2010 and 2011 conferences on computer vision and image analysis of art, and founding general chair of the OSA Digital image processing and analysis (DIPA) conference.