Abstract
While some models for visual attention represent computational models of human attention and are directly based on biological evidence24,11,10,31,1, other systems focus more on the computational aspects of the simulation and have been recognized as important components for tackling practical problems and for analyzing real images (e.g. 35,19,36). These attentional systems, thanks also to the increasing interest in neural networks research, have been largely implemented by Artificial Neural Networks (ANNs). There are some practical considerations that encourage to use ANNs, most of which are related to their speed and flexibility. Commonly used Artificial Neural Networks have been widely demonstrated to be tolerant to noise, some scaling rotation and translation factors. They involve phases to dynamically adapt the network to the environment (plasticity). ANNs are also characterized by the intrinsic capability of processing simultaneously different sources of information such as edges and regions, so as to overcome the difficult problem of integrating the results produced by different algorithms, each one working on a different kind of information.
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References
S. Ahmad, “VISIT: an efficient computational model of human visual attention”, Ph.D. Thesis, University of Illinois at Urbana Champaign, and Technical Report TR-91-049, ICSI, Berkely CA, 1991.
R. Bajcsy, D.A. Rosenthal, “Visual and conceptual focus of attention”, Structured Computer Vision, S. Tanimoto and A. Klinger eds., Academic Press, pp. 133–149, 1980.
F. Bergholm, “Edge focusing”,IEEE Transactions on PAMI, vol. 9, no. 6, pp. 726–741, 1987.
R.J. Watt, “Scanning from coarse to fine spatial scales in the human visual system after the onset of a stimulus”, Journal of Optical Society of America, vol. 4, no. 10, pp. 2006–2021, 1987.
P. J. Burt, E. H. Adelson, “The Laplacian Pyramid as a Compact Image Code”, IEEE Trans. on Comm., Vol. COM-31, n. 4, pp. 532–540, 1983.
P. J. Burt, C. H. Anderson, J. O. Sinninger, G. van der Wal, “A Pipeline Pyramid Machine”, Pyramidal Systems for Computer Vision, V. Cantoni and S. Levialdi eds., pp. 133–152, Springer-Verlag, Berlin, 1986.
V. Cantoni, S. Levialdi, “Pyramidal Systems for Computer Vision”, Springer-Verlag, 1987.
V. Cantoni, M. Ferretti, “Pyramidal Architectures for Computer Vision”, Plenum Press, New York, 1994.
V. Cantoni, A. Petrosino, “Neural Recognition in a Pyramidal Structure”, IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 472–480, March 2002.
K.R. Cave, J.M. Wolfe, “Modeling the role of parallel processing in visual search”, Cognitive Psychology, vol. 22, pp. 225–271, 1990.
D. Chapman, “Vision, instruction and action”, Ph.D. Thesis, AI Laboratory, MIT, Technical Report AI-TR-1204, April 1990.
C. R. Dyer, “Multiscale Image Understanding”, in Parallel Computer Vision, L. Uhr ed., Academic Press, 1987.
O. K. Ersoy, D. Hong, “Parallel, self-organizing, hierarchical neural networks”,IEEE Trans, on Neural Networks, 1, pp. 167–178, 1990.
K. Fukushima, “Neocognitron: a hierarchical neural network capable of visual pattern recognition”, Neural Networks, 1, pp. 119–130, 1998.
S. Geman, E. Bienenstock, R. Dourstat, “Neural networks and the bias/variance dilemma”,Neural Computation, 4, 1, pp. 1–58, 1992.
M. Gori, A. Tesi, “On the problem of local minima in backpropagation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, n. 1, pp. 76–86, 1992.
M. Bianchini, M. Gori, “ Optimal Learning in artificial neural networks: a review of theoretical results”, Neurocomputing, vol. 13, no. 5, pp. 313–346, October 1996.
M. Gori, F. Scarselli, “ Are multilayer networks adequate for pattern recognition and verification?”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1121–1132, 1998.
G.-J. Giefing, H. Janssen, H. Mallot, “Saccadic object recognition with an active vision system”, Proc. of Internat. Conf. on Pattern Recognition, pp. 664–667, 1992.
R. C. Gonzalez and P. Wintz, “Digital Image Processing”, Addison-Wesley,1987.
S. Haykin, “Neural Networks: a Comprehensive Foundation”, Macmillan/IEEE Press, 1994.
V. Honavar, L. Uhr, “Brain-structured Connectionist Networks that Perceive and Learn”, Connection Science, Vol. 1, N. 2, pp. 139–159, 1989.
R. Hummel, “The Scale-space Formulation of Pyramid Data Structures”, inParallel Computer Vision, L. Uhr ed., Academic Press, 1987.
C. Koch, S. Ullman, “Shifts in selective visual attention: towards the underlying neural circuitry”, Matters of Intelligence, L.M. Vaina ed., pp. 115-141, Reidel Publishing, 1987.
D. R. Lovell, T. Downs, A.-C. Tsoi, “Evaluation of the Neocognitron”, IEEE Transactions on Neural Networks, vol. 8, n. 5, pp. 1098–1105, 1997.
D. Marr, E. C. Hildreth, “Theory of edge detection”, Proc. R. Soc. London, vol. B-207, pp. 187–217, 1980.
M. R. J. McQuoid, “Neural Ensembles: Simultaneous Recognition of Multiple 2-D Visual Objects”, Neural Networks, Vol. 6, pp. 907–917, 1993.
Y. le Cun, “Generalization and network design strategies”, in Connectionism in Perspective, R. Pfeifer et al. ed.s, pp. 143–155, Elsevier, Amsterdam, 1989.
Y. le Cun, Y. Bengio, “Convolutional networks for images, speech, and times series”, in The Handbook of Brain Science and Neural Networks, M. Arbib ed., Cambridge, MA: MIT Press, pp. 225–258, 1995.
C. Neubauer, “Evaluation of convolutional neural networks for visual recognition”, IEEE Transactions on Neural Networks, vol. 9, n. 4, pp. 685–696, 1998.
B. Olshausen, C. Anderson, D. van Essen, “A neural model of visual attention and invariant pattern recognition” , Caltech, Computational and Neural System Program, CNC Memo 18, August 1992.
S. J. Perantonis, P. J. G. Lisboa, “Translation, rotation and scale invariant pattern recognition by high-order neural networks and moment classifiers”, IEEE Transactions on Neural Networks, 3, 2, pp. 241–251, 1992.
A. Rosenfeld, “Multiresolution Image Processing and Analysis”, Springer-Verlag, 1984.
D. E. Rumelhart, G. E. Hinton, R. J. Williams, “Learning Internal Representations by Error Propagation”,Parallel Distribuited Processing: Exploration in the the Microstructure of Cognition, cap. 8, 1986.
P.A. Sandon, “Simulating visual attention”, Journal of Cognitive Neuroscience, vol. 2, no. 3, pp. 213–231, 1990.
A. Shashua, S. Ullman, “Structured saliency: the detection of globally salient structures using a locally connected network”, Proc. of the International Conference on Computer Vision, pp. 321–327, 1988.
L. Uhr, “Layered Recognition Cone Networks that Preprocess, Classify and Describe”, IEEE Transactions on Computer, vol. 21, pp. 758–768, 1972.
L. Uhr, “Pyramid Multi-Computer Structures and Augmented Pyramids”, in Computing Structures for Image Processing, M. Duffed., Academic Press, 1983.
S. G. Ziavras, M. A. Siddiqui, “Pyramid Mappings onto Hypercubes for computer Vision: Connection Machine Comparative Study”, Concurrency: Practice and Experience, Vol. 5(6), pp. 471–489, 1993.
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Petrosino, A. (2002). Attentional Pyramidal Neural Mechanisms. In: Cantoni, V., Marinaro, M., Petrosino, A. (eds) Visual Attention Mechanisms. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0111-4_24
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DOI: https://doi.org/10.1007/978-1-4615-0111-4_24
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