Mixture Models for Image Representation

(with Allan Jepson Project Technical Report ARK96-PUB-54, March 1996.)

We consider the estimation of local grey-level image structure in terms of a layered representation. This type of representation has recently been successfully used to segment various objects from clutter using either optical flow or stereo disparity information. We argue that the same type of representation is useful for grey-level data in that it allows for the estimation of properties for each of several different components without prior segmentation. Our emphasis in this paper is on the process used to extract such a layered representation from a given image. In particular, we consider a variant of the EM-algorithm for the estimation of the layered model, and consider a novel technique for choosing the number of layers to use. We briefly consider the use of a simple version of this approach for image segmentation, and suggest two potential applications to the ARK (Autonomous Robot for a unKnown Environment) project.

Related Publications

A. D. Jepson and M. J. Black, Mixture Models for Image Representation, PRECARN ARK Project Technical Report ARK96-PUB-54, March 1996.