From Human Eye Fixation to Human-like Autonomous Artificial Vision

  • Viachaslau KachurkaEmail author
  • Kurosh Madani
  • Cristophe Sabourin
  • Vladimir Golovko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)


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.


Autonomous vision Center-surround saliency Evolutionary optimization Eye fixation Human-like visual attention 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Viachaslau Kachurka
    • 1
    • 2
    Email author
  • Kurosh Madani
    • 1
  • Cristophe Sabourin
    • 1
  • Vladimir Golovko
    • 2
  1. 1.LISSI / EA 3956 Laboratory, Senart-FB Institute of TechnologyUniversity Paris-Est CreteilLieusaintFrance
  2. 2.Neural Networks Laboratory, Intelligent Information Technologies DepartmentBrest State Technical UniversityBrestBelarus

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