chaotic phase synchronization for visual selection
DESCRIPTION
International Joint Conference on Neural Networks – IJCNN 2009. Chaotic Phase Synchronization for Visual Selection. Fabricio A. Breve¹ [email protected] Liang Zhao¹ [email protected] Marcos G. Quiles¹ [email protected] Elbert E. N. Macau² [email protected]. - PowerPoint PPT PresentationTRANSCRIPT
Chaotic Phase Synchronization for Visual Selection
Fabricio A. Breve¹ [email protected] Zhao¹ [email protected] G. Quiles¹ [email protected] E. N. Macau² [email protected]
¹ Department of Computer Science, Institute of Mathematics and Computer Science, University of São Paulo, São Carlos-SP, Brazil
² National Institute for Space Research, São José dos Campos-SP, Brazil
International Joint Conference on Neural Networks – IJCNN 2009
Outline
Visual Selection Chaotic Phase Synchronization Model Description Computer Simulations
Artificial imagesReal-world images
Conclusions
Visual Selection
Capacity developed by living systems to select just relevant environmental information Identifies the region of the visual input that will
reach awareness level (focus of attention) while irrelevant information is suppressed
[FRI01, KIM07, BUI06, NIE94, SHI07, TSO92, ITT01, CAR04]
Chaotic Phase Synchronization
Two oscillators are called phase synchronized if their phase difference is kept bounded while their amplitudes may be completely uncorrelated
M|<| 21 tas
[PIK01, ROS96]
Chaotic Phase Synchronization Two coupled Rössler oscillators:
)(= 1,22,11,21,21,21,2 xxkzyx 1,21,21,21,2 = ayxy )(= 1,21,21,2 cxzbz
22= yxA [ROS96, OSI97] 0.98=1 1.02=2
Model Description Two dimensional network of Rössler Oscillators:
,= ,,,,,,, jijijijijijiji xxkzyx ,= ,,,, jijijiji ayxy
).(= ,,, cxzbz jijiji
)(= ,11,,1;1,, jijijijiji xxx
)( ,1,,;1, jijijiji xx )( ,11,,1;1, jijijiji xx
)( ,1,,1;, jijijiji xx )( ,1,,1;, jijijiji xx
)( ,11,,1;1, jijijiji xx )( ,1,,;1, jijijiji xx)( ,11,,1;1, jijijiji xx
.0,,),(),(1,
=,;, otherwiseqptocoupledisjioscillatorif
qpji
,)(max)(min
= ,, C
CC jiji
,||= ,,
d
avgdji
dji FFC
..1= ,
=
1=
=
1=
dji
Mj
j
Ni
i
davg F
MNF
]2
12
[1,
ji
Model Description
Oscillators which corresponds to pixels with: higher contrast
Negative coupling strength tends to zero They will be synchronized in phase
lower contrast Negative coupling strength is higher They will repel each other.
After some time, only the oscillators corresponding to the salient object will remain with their trajectories synchronized in phase while the other objects will have trajectories with different phases.
ARTIFICIAL IMAGESComputer Simulations
Artificial Image with high contrast
Artificial Image with high contrast
1.0=
Artificial Image with low contrast
Artificial Image with low contrast
1.0=
Artificial Image with low contrast
4.0=
REAL-WORLD IMAGESComputer Simulations
Real-world Image: Bird
Real-world Image: Dog
Real-world Image: Flower
Conclusions The proposed model can be applied to object
selection Chaotic Phase Synchronization
Used to discriminate the salient object from the visual input while keeping the non-salient, or less salient, objects unsynchronized
Main Advantages: Robustness
Requires small coupling strength Biological inspiration
Observed in nonidentical systems Believed to be the key mechanism for neural integration in
brain [VAR01]
Acknowledgements
This work was supported by the State of São Paulo Research Foundation (FAPESP) and the Brazilian National Council of Technological and Scientific Development (CNPq)
References [FRI01] P. Fries, J. H. Reynolds, A. E. Rorie, and R. Desimone, “Modulation of oscillatory neuronal
synchronization by selective visual attention,” Science, vol. 291, no. 5508, pp. 1560–1563, 2001. [KIM07] Y. J. Kim, M. Grabowecky, K. A. Paller, K. Muthu, and S. Suzuki, “Attention induces synchronization-
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[NIE94] E. Niebur and C. Koch, “A model for neuronal implementation of selective visual attention based on temporal correlation among neurons,” Journal of Computational Neuroscience, vol. 1, pp. 141–158, 1994.
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[ROS96] M. G. Rosenblum, A. S. Pikovsky, and J. Kurths, “Phase synchronization of chaotic oscillators,” Phisical Review Letters, vol. 76, no. 7, pp. 1804–1807, March 1996.,
[OSI97] G. V. Osipov, A. S. Pikovsky, M. G. Rosenblum, and J. Kurths, “Phase synchronization effects in a lattice of nonidentical r¨ossler oscillators,” Phys. Rev. E, vol. 55, no. 3, pp. 2353–2361, Mar 1997.
[VAR01] F. Varela, J.-P. Lachaux, E. Rodriguez, and J. Martinerie, “The brainweb: Phase synchronization and large-scale integration,” Nature Reviews Neuroscience, vol. 2, pp. 229–239, April 2001.
Chaotic Phase Synchronization for Visual Selection
Fabricio A. Breve¹ [email protected] Zhao¹ [email protected] G. Quiles¹ [email protected] E. N. Macau² [email protected]
¹ Department of Computer Science, Institute of Mathematics and Computer Science, University of São Paulo, São Carlos-SP, Brazil
² National Institute for Space Research, São José dos Campos-SP, Brazil
International Joint Conference on Neural Networks – IJCNN 2009