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The dynamic model of lateral inhibition network and it is application

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Abstract

Concentrate on the computational model of visual attention, in this paper, we present the electric circuit model of the human retina cells on the base of lateral inhibit network and then deduce the mathematic formulas to define the static and dynamic processes of the network. The learning algorithm is also discussed to get right model parameters. The model reveals the case of the human biological character on image and video processing. Specifically on video processing, the convolution integral function which shows the neural computing process of the human eyes is developed based on the dynamical equation and the circuit principle. Some examples are also introduced to demonstrate the neural computing process of the model on the static image and video processing.

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References

  1. Bleyer M, Gelautz M (2005) Graph-based surface reconstruction from stereo pairs using image segmentation. In: Proceedings of SPIE, vol 5665, video metrics VIII, January 2005, pp 288–299

  2. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577

    Article  Google Scholar 

  3. Anllo-Vento L, Hillyard SA (1996) Selective attention to the color and direction of moving stimuli: electrophysiological correlates of hierarchical feature selection. Percept Psychophys 58(2):191–206

    Article  Google Scholar 

  4. Hirsch JA, Gilbert CD (1991) Synaptic physiology of horizontal connection in the cats visual cortex. J Neurosci 11(6):1800–1809

    Google Scholar 

  5. Behrmann M, Zemel RS, Mozer MC (2000) Occlusion, symmetry, and object-based attention: reply to Saiki. J Exp Psych Hum Percept Perf 26(4):1497–1505

    Article  Google Scholar 

  6. Mao Z-H, Steve G (2007) Dynamics of winner-take-all competition in recurrent neural networks with lateral inhibition. IEEE Trans Neural Netw 18:55–68

    Article  Google Scholar 

  7. Fern Sandez-Caballeroa A, Mirab JM, Delgadob AE, Miguel A (2003) Lateral interaction in accumulative computation: a model for motion detection. J Nero Comput 50:314–3641

    Google Scholar 

  8. Bhuiyan MS, Iwahori Y, Matsuo H et al (1997) Optimal edge detection under difficult imaging conditions, Lecture notes in computer science. Springer, Berlin, pp 25–32

  9. Ana JoseMira, Delgado E, Fernandez-Caballerob Antonio (2004) Knowledge modeling for the motion detection task: the algorithmic lateral inhibition method. Expert Syst Appl 27:169–185

    Article  Google Scholar 

  10. Maria TL, Fern AC et al (2006) Algorithmic lateral inhibition method in dynamic and selective visual attention task: application to moving objects detection and labeling. Expert Syst Appl 31:570–594

    Article  Google Scholar 

  11. Monga V, Geisler ÓWS, Evans BL et al (2003) Linear color separable human visual system models for vector error diffusion half toning. IEEE Signal Process Lett 10(4):93–97

    Article  Google Scholar 

  12. Ke GaoYangLiYan-junZhang (2006) Application of human vision bionics in detection, estimation and tracking for photoelectric target. Aero Weaponry 1:22–25

    MathSciNet  Google Scholar 

  13. Waxman AM, Gove AN, Fay DA, Gove AN (1997) Color night vision: opponent processing in the fusion of visible and IR imagery. Neural Net 1:1–6

    Google Scholar 

  14. Miguel A-C, Fernandez A, Mira J, Delgado AE (2003) Spatiotemporal shape building from image sequences using lateral interaction in accumulative computation. Pattern Recogn 36(5):1131–1142

    Article  MATH  Google Scholar 

  15. Fernandez-Caballero A, Mira J, Fernandez MA, Lopez MT (2001) Segmentation from motion of non-rigid objects by neuronal lateral interaction[J]. Pattern Recogn Lett 22:1517–1524

    Article  MATH  Google Scholar 

  16. Durrant Simon, Feng Jian (2006) Negatively correlated firing: the functional meaning of lateral inhibition within cortical columns. Biol Cybern 93:431–453

    Article  MathSciNet  Google Scholar 

  17. Jain AK, Yu B (1998) Automatic text location in images and video frames. Pattern Recogn 31(12):2055–2076

    Article  Google Scholar 

  18. Gray CM, Konig P, Engel AK et al (1989) Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338:334–337

    Article  Google Scholar 

  19. Baeker G, Mertsehing B (2000) Integrating depth and motion into the attention control of active vision system. In: Baratoff G, Hanuman (eds) Dynamic perception. Infix, St. Augustin, pp 69–74

  20. Pease P (1978) On color mach bands. Vision Res 18:751–755

    Article  Google Scholar 

  21. Gur M, Syrkin G (1993) Color enhances Mach band detection threshold and perceived rightness. Vision Res 33:2313–2319

    Article  Google Scholar 

  22. Suthaharan S (2003) Perceptual, quality metric for digital video coding. IEEE Elect Lett 39(5):431–433

    Article  Google Scholar 

  23. Karunaseker SA, Kingsburyn NG (1995) A distortion measure for blocking artifacts in image based on human visual sensitivity. IEEE Trans Image Process 4(6):713–724

    Article  Google Scholar 

  24. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):1–45 (Article 13)

    Article  Google Scholar 

  25. Moeslund TB, Hilton A, Kruger V (2006) A survey of advances in vision-based human motion capture and analysis. Comput Vis Image Underst 104:90–126

    Article  Google Scholar 

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Correspondence to Li Feng.

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Feng, L., Xiaoqiang, L. The dynamic model of lateral inhibition network and it is application. Neural Comput & Applic 22 (Suppl 1), 125–131 (2013). https://doi.org/10.1007/s00521-013-1379-x

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