Robust pose estimation and recognition using non-gaussian modeling of appearance subspaces.

We present an original appearance model that generalizes the usual Gaussian visual subspace model to non-Gaussian and nonparametric distributions. It can be useful for the modeling and recognition of images under difficult conditions such as large occlusions and cluttered backgrounds. Inference under the model is efficiently solved using the mean shift algorithm.