=. Deptht1, depth ( { t r e e i n T1 } ) ) = 15

=. Deptht2, depth ( { t r e e i n T2 } ) ) = 15

=. Deptht3, depth ( { t r e e i n T2 } ) ) = 15

V. Kachurka, K. Madani, C. Sabourin, and V. Golovko, From Human Eye Fixation to Human-like Autonomous Artificial Vision " in proc. of International Work-Conference on Artificial Neural Networks as book Advances in Computational Intelligence, LNCS, vol.9094, pp.2015-171

V. Kachurka, K. Madani, C. Sabourin, and V. Golovko, A Statistical Approach to Human-Like Visual Attention and Saliency Detection for Robot Vision: Application to Wildland Fires??? Detection, Proc. of 8th International Conference on Neural Network and Artificial Intelligence Communications in Computer and Information Science, pp.3-6, 2014.
DOI : 10.1007/978-3-319-08201-1_12
URL : https://hal.archives-ouvertes.fr/hal-01568466

V. Kachurka, Design Patterns in N-tier Architecture, Proc. of 15th International Ph.D. Workshop (OWD'2013), pp.94-97, 2013.

1. K. Madani, V. Kachurka, C. Sabourin, and V. Golovko, A human-like visual-attention-based artificial vision system for wildland firefighting assistance, Applied Intelligence, 2017.
DOI : 10.3390/e14091606
URL : https://hal.archives-ouvertes.fr/hal-01681960

K. Madani, V. Kachurka, C. Sabourin, and V. Golovko, A soft-computing-based approach to artificial visual attention using human eye-fixation paradigm: toward a human-like skill in robot vision, Soft Computing, 2017.
DOI : 10.1109/ICCV.2013.26

S. Achanta, F. Hemami, S. Estrada, and . Susstrunk, Frequency-tuned salient region detection, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.1597-1604, 2009.
DOI : 10.1109/CVPR.2009.5206596
URL : http://infoscience.epfl.ch/record/135217/files/1708.pdf?version=1

H. David, G. E. Ackley, &. Hinton, J. Terrence, and . Sejnowski, A learning algorithm for Boltzmann machines, Cognitive science, vol.9, issue.1, pp.147-169, 1985.

. Damminda-alahakoon, K. Saman, &. Halgamuge, and . Srinivasan, Dynamic self-organizing maps with controlled growth for knowledge discovery, IEEE Transactions on Neural Networks, vol.11, issue.3, pp.601-614, 2000.
DOI : 10.1109/72.846732

V. Amarger, C. Dominik-maximilián-ramík, K. Sabourin, R. Madani, L. Moreno et al., Spherical coordinates framed RGB color space dichromatic reflection model based image segmentation: Application to wildland fires' outlines extraction, 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), pp.19-24, 2012.
DOI : 10.1109/IPTA.2012.6469529
URL : https://hal.archives-ouvertes.fr/hal-01680014

R. John and . Anderson, Cognitive psychology and its implications, WH FreemanTimes Books, 1990.

C. Richard, &. Atkinson, M. Richard, and . Shiffrin, Human memory: A proposed system and its control processes, Psychology of learning and motivation, vol.2, pp.89-195, 1968.

B. Maya, &. Mathur, B. David, and . Reichling, Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley, Cognition, vol.146, pp.22-32, 2016.

]. Matsugu, K. Mori, Y. Mitari, and &. Kaneda, Subject independent facial expression recognition with robust face detection using a convolutional neural network, Neural Networks, vol.16, issue.5-6, pp.555-559, 2003.
DOI : 10.1016/S0893-6080(03)00115-1

A. George and . Miller, WordNet: a lexical database for English, Communications of the ACM, vol.38, issue.11, pp.39-41, 1995.

M. Minsky, A FRAMEWORK FOR REPRESENTING KNOWLEDGE, 1975.
DOI : 10.1016/B978-1-4832-1446-7.50018-2

]. Moreno, M. Grana, and . Madani, Image segmentation by spherical coordinates, Proceedings of the 11th International Conference on Pattern Recognition and Information Processing (PRIP'11), pp.112-115, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01680028

V. Nastase, M. Strube, and B. Börschinger, Cäcilia Zirn & Anas Elghafari. WikiNet: A Very Large Scale Multi- Lingual Concept Network, LREC, 2010.

V. Navalpakkam and &. Laurent-itti, An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.2049-2056, 2006.
DOI : 10.1109/CVPR.2006.54

G. Scragg, Semantic nets as memory models, Charniak and Wilks, pp.101-127, 1976.

C. Shen and &. Zhao, Webpage Saliency, European Conference on Computer Vision, pp.33-46, 2014.
DOI : 10.1007/978-3-319-10584-0_3

K. Simonyan and &. Andrew-zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint, 2014.

M. Skubic, D. Noelle, M. Wilkes, K. Kawamura, &. James et al., A biologically inspired adaptive working memory for robots, AAAI Fall Symp., Workshop on the Intersection of Cognitive Science and Robotics: From Interfaces to Intelligence, 2004.

F. John and . Sowa, Principles of semantic networks: Explorations in the representation of knowledge, 2014.

J. Sudol, O. Dialameh, C. Blanchard, and T. Dorcey, Looktel?A comprehensive platform for computeraided visual assistance, Computer Vision and Pattern Recognition Workshops 2010 IEEE Computer Society Conference on, pp.73-80, 2010.
DOI : 10.1109/cvprw.2010.5543725

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-9, 2015.
DOI : 10.1109/CVPR.2015.7298594
URL : http://arxiv.org/pdf/1409.4842

W. Benjamin and . Tatler, The central fixation bias in scene viewing: Selecting an optimal viewing position independently of motor