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Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 354785, 11 pages
http://dx.doi.org/10.1155/2012/354785
Research Article

Multilevel Cognitive Machine-Learning-Based Concept for Artificial Awareness: Application to Humanoid Robot Awareness Using Visual Saliency

Images, Signals and Intelligence Systems Laboratory (LISSI/EA 3956) and Senart-FB Institute of Technology, University Paris-EST Créteil (UPEC), Bât.A, avenue Pierre Point, 77127 Lieusaint, France

Received 11 March 2012; Revised 12 May 2012; Accepted 20 May 2012

Academic Editor: Qiangfu Zhao

Copyright © 2012 Kurosh Madani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [4 citations]

The following is the list of published articles that have cited the current article.

  • Dominik Maximilián Ramík, Kurosh Madani, and Christophe Sabourin, “From Visual Patterns to Semantic Description: a Cognitive Approach Using Artificial Curiosity as the Foundation,” Pattern Recognition Letters, 2013. View at Publisher · View at Google Scholar
  • Yuantao Chen, Weihong Xu, Fangjun Kuang, and Shangbing Gao, “The Research and Application of Visual Saliency and Adaptive Support Vector Machine in Target Tracking Field,” Computational and Mathematical Methods in Medicine, vol. 2013, pp. 1–8, 2013. View at Publisher · View at Google Scholar
  • Dominik M. Ramík, Kurosh Madani, and Christophe Sabourin, “A Soft-Computing basis for robots’ cognitive autonomous learning,” Soft Computing, 2014. View at Publisher · View at Google Scholar
  • M. J. Mahmoodabadi, M. Taherkhorsandi, and A. Bagheri, “Pareto Design of State Feedback Tracking Control of a Biped Robot via Multiobjective PSO in Comparison with Sigma Method and Genetic Algorithms: Modified NSGAII and MATLAB’s Toolbox,” The Scientific World Journal, vol. 2014, pp. 1–8, 2014. View at Publisher · View at Google Scholar