Linear and incremental acquisition of invariant shape models from image sequences

The authors show how to automatically acquire similarity-invariant shape representations of objects from noisy image sequences under a weak perspective. The incremental nature of the method makes it possible to process images one at a time, moving away from the storage-intensive batch methods of the past. It is based on the observation that the trajectories that points on the object form in weak-perspective image sequences are linear combinations of three of the trajectories themselves, and that the coefficients of the linear combinations represent shape in an affine-invariant basis. A nonlinear but numerically sound preprocessing state is added to improve the accuracy of the results even further. Experiments showed that attention to noise and computational techniques improved the shape results substantially with respect to previous methods