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This paper presents a multi-differential neuromorphic approach to motion detection. The model is based evidence for a differential operators interpretation of the properties of the cortical motion pathway. We discuss how this strategy, which provides a robusl measure of speed for a range of types of image motion using a single compulational mechanism, forms a usful framework in which to develop future neuromorphic motion systems. We also discuss both our approaches to developing computational motion models, and constraints in the design strategy for transferring motion models to other domains of early visual processing.

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Many modern imaging and surveillance systems contain more than one sensor. For example, most modern airborne imaging pods contain at least visible and infrared sensors. Often these systems have a single display that is only capable of showing data from either camera, and thereby fail to exploit the benefit of having simultaneous multi-spectral data available to the user. It can be advantageous to capture all spectral features within each image and to display a fused result rather than single band imagery. This paper discusses the key processes necessary for an image fusion system and then describes how they were implemented in a real-time, rugged hardware system. The problems of temporal and spatial misalignment of the sensors and the process of electronic image warping must be solved before the image data is fused. The techniques used to align the two inputs to the fusion system are described and a summary is given of our research into automatic alignment techniques. The benefits of different image fusion schemes are discussed and those that were implemented are described. The paper concludes with a summary of the real-time implementation of image alignment and image fusion by Octec and Waterfall Solutions and the problems that have been encountered and overcome.