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When designing camouflage it is important to understand how the human visual system processes the information to discriminate the target from the background scene. A vision model has been developed to compare two images and detect differences in local contrast in each spatial frequency channel. Observer experiments are being undertaken to validate this vision model so that the model can be used to quantify the relative significance of different factors affecting target conspicuity. Synthetic imagery can be used to design improved camouflage systems. The vision model is being used to compare different synthetic images to understand what features in the image are important to reproduce accurately and to identify the optimum way to render synthetic imagery for camouflage effectiveness assessment. This paper will describe the vision model and summarise the results obtained from the initial validation tests. The paper will also show how the model is being used to compare different synthetic images and discuss future work plans.

A modification of the Blahut algorithm is proposed for decoding of Reed-Solomon codes beyond half the minimum distance. An effective method is offered for the searching of unknown discrepancies needed for analytical continuation of the Berlekamp-Massey algorithm through two additional iterations. This reduces the search time by 2(q-1)n/((n+t+1)(n-t)) times, compared with the Blahut algorithm. An architecture of a searcher for unknown discrepancies is given. The coding gain of the proposed algorithm is shown for some practical codes.