Spatio-Chromatic Decorrelation by Shift-Invariant Filtering

In this paper we derive convolutional filters for colour image whitening and decorrelation. Whilst whitening can be achieved via eigendecomposition of the image patch covariance, this operation is neither efficient nor biologically plausible. Given the shift invariance of image statistics, the covariance matrix contains repeated information which can be eliminated by solving directly for a per pixel linear operation (convolution). We formulate decorrelation as a shift and rotation invariant filtering operation and solve directly for the filter shape via non-linear least squares. This results in opponent-colour lateral inhibition filters which resemble those found in the human visual system. We also note the similarity of these filters to current interest point detectors, and perform an experimental evaluation of their use in this context. 1

Multimodal Computational Attention for Scene Understanding

Robotic systems have limited computational capacities. Hence, computational attention models are important to focus on specific stimuli and allow for complex cognitive processing. For this purpose, we developed auditory and visual attention models that enable robotic platforms to efficiently explore and analyze natural scenes. To allow for attention guidance in human-robot interaction, we use machine learning to integrate the influence of verbal and non-verbal social signals into our models