Abstract
Artificial visual attention is one of the key methodologies inspired from nature that can lead to robust and efficient visual search by machine vision systems. A novel approach is proposed for modeling of top-down visual attention in which separate saliency maps for the two attention pathways are suggested. The maps for the bottom-up pathway are built using unbiased rarity criteria while the top-down maps are created using fine-grain feature similarity with the search target as suggested by the literature on natural vision. The model has shown robustness and efficiency during experiments on visual search using natural and artificial visual input under static as well as dynamic scenarios.
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Aziz, M.Z., Mertsching, B. (2008). Visual Search in Static and Dynamic Scenes Using Fine-Grain Top-Down Visual Attention. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_1
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DOI: https://doi.org/10.1007/978-3-540-79547-6_1
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