Abstract
A region-based approach towards modelling of bottom-up visual attention is proposed with an objective to accelerate the internal processes of attention and make its output usable by the high-level vision procedures to facilitate intelligent decision making during pattern analysis and vision-based learning. A memory-based inhibition of return is introduced in order to handle the dynamic scenarios of mobile vision systems. Performance of the proposed model is evaluated on different categories of visual input and compared with human attention response and other existing models of attention. Results show success of the proposed model and its advantages over existing techniques in certain aspects.
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Aziz, M.Z., Mertsching, B. (2009). Early Clustering Approach towards Modeling of Bottom-Up Visual Attention. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_40
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DOI: https://doi.org/10.1007/978-3-642-04617-9_40
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