Software Library for Appearance Matching (SLAM) *

The SLAM software package has been developed for appearance learning and matching problems in computational vision. Appearance learning involves use of principal component analysis for compression of a large input image set to a compact low-dimensional subspace, called the eigenspace, in which the images reside as parameterized manifolds. SLAM enables the user to obtain this parametric representation by providing modules for eigenspace computation, projection of images to eigenspace, and interpolation of multivariate manifolds through the projections. Appearance matching is done by searching for a projection in eigenspace closest to a novel input projection. Algorithms have been provided for performing this search in real-time, even with huge datasets. Benchmarks demonstrate the suitability of SLAM for application to real-world problems. The functionality has been made available to the user through an X/Motif Graphical User Interface along with commandline programs and a C++ class library. Use of object oriented techniques provides an easy to use and extensible Application Programming Interface.