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The use of omnidirectional cameras has had a significant impact on the success of vision systems for video surveillance and autonomous robot navigation. Typically images obtained from such cameras are transformed to sparse panoramic images that are interpolated prior to low level image processing. We present a graph theoretic approach that enables image processing techniques, principally feature extraction, to be performed directly on sparse panoramic images, avoiding the need for image interpolation. We thus aim to reduce the computational overheads of processing images arising from omnidirectional cameras, whilst retaining accuracy sufficient for application to real-time robot vision.