A novel visual perception framework

This paper presents a unified framework for online visual perception. The twin problems of visual feature extraction and representation are explicitly addressed. Simple paradigms for supervised and unsupervised feature extraction are presented to represent the extremes in visual perception system design. Visual feature representation is addressed through a combination of Isomap, a non-linear dimensionality reduction algorithm, and Expectation Maximization (EM), a statistical learning scheme. A joint probability distribution of this representation is computed offline based on existing training data. Example applications based on real visual data from heterogenous, unstructured environments demonstrate the versatility of the generative models.