Perceptual Science Series
December 1, 2003 at 2:00 p.m.
Psychology Room 101, Busch Campus
Columbia University, Department of Computer Science
Signal-Theoretic Representations of Appearance
Many problems in computer vision and computer graphics require compact
representations of the appearance of objects, and the mathematical
algorithms to manipulate them. For instance, consider the space of
images of an object under all possible lighting conditions---something
we need to identify or recognize objects in a lighting-insensitive
way. Since the illumination can in principle come from anywhere, the
appearance manifold would seem to be infinite-dimensional. However,
one can find lower-dimensional and more compact structures that lead
to efficient algorithms.
In this talk, we discuss a signal-theoretic approach to representing
appearance, where the illumination and reflection function are
signals and filters, and we apply many signal-processing tools such as
convolution and wavelet-based representation and non-linear
approximation. These representations and tools are applicable to a
variety of problems in computer graphics and vision, and we will
present examples in real-time rendering in computer graphics, as well
as image-based and inverse problems, multiple scattering for
volumetric effects, and recognition with complex lighting and specularities.