In contrast to the traditional approach of recognizing objects based on their shapes, we formulate the recognition problem as one of matching appearances. For any given vision task, all possible appearance variations define its visual workspace. A set of images is obtained by coarsely sampling the workspace. The image set is compressed to obtain a low-dimensional subspace, called the eigenspace, in which the visual workspace is represented as a continuous appearance manifold. Given an unknown input image, the recognition system first projects the image to the eigenspace. The parameters of the vision task are recognized based on the exact position of the projection on the appearance manifold.

The proposed appearance representation has several applications in visual perception. As examples, a real-time recognition system with 100 complex objects, an illumination planning technique for robust object recognition, and a real-time visual positioning and tracking system have been developed. The simplicity and generality of the proposed ideas have led to the development of a software library for appearance modeling and matching (SLAM). SLAM has been been used by over 100 research laboratories to develop techniques for appearance matching. It has also be used by several companies to solve real-world machine vision problems.