Abstract
Fitting the skills of the natural vision is an appealing perspective for artificial vision systems, especially in robotics applications where visual perception of the surrounding environment is a key requirement. Focusing on the visual attention dilemma for autonomous visual perception, in this work we propose a model for artificial visual attention combining a statistical foundation of visual saliency and a genetic optimization. The computational issue of our model relies on center-surround statistical features calculations and a nonlinear fusion of different resulting maps. Statistical foundation and bottom-up nature of the proposed model provide as well the advantage to make it usable without needing prior information as a comprehensive solid theoretical basement. The eye-fixation paradigm has been considered as evaluation benchmark providing MIT1003 and Toronto image datasets for experimental validation. The reported experimental results show scores challenging currently best algorithms used in the aforementioned field with faster execution speed of our approach.
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Kachurka, V., Madani, K., Sabourin, C., Golovko, V. (2015). From Human Eye Fixation to Human-like Autonomous Artificial Vision. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_15
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