Yıl: 2019 Cilt: 23 Sayı: 2 Sayfa Aralığı: 611 - 616 Metin Dili: İngilizce DOI: DOI: 10.19113/sdufenbed.570597 İndeks Tarihi: 23-10-2020

Edge Detection Using Integrate and Fire Neuron Model

Öz:
Edge detection is one of the most basic stages of image processing andhave been used in many areas. Its purpose is to determine the pixels formed theobjects. Many researchers have aimed to determine objects' edges correctly, like asthey are determined by the human eye. In this study, a new edge detection techniquebased on spiking neural network is proposed. The proposed model has a differentreceptor structure than the ones found in literature and also does not use gray levelvalues of the pixels in the receptive field directly. Instead, it takes the gray leveldifferences between the pixel in the center of the receptive field and others as input.The model is tested by using BSDS train dataset. Besides, the obtained results arecompared with the results calculated by Canny edge detection method.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • [1] Canny, J. A. 1986. Computational Approach to Edge-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679- 698.
  • [2] Demirci, R. 2007. Similarity Relation MatrixBased Color Edge Detection. AEU-International Journal of Electronics and Communications, 61(7), 469-477.
  • [3] Gonzalez, R.C., Woods, R.E. 2008. Digital Image Processing, 3rd Ed. Pearson/Prentice Hall, New Jersey.
  • [4] Wandell, B. A. 1995. Foundations of Vision. Sinauer Associates, Inc., Sunderland, MA, 476s.
  • [5] Kaiser, P. K., Boynton, R. 1996. Human Color Vision, 2nd edition. Optical Society of America, Washington, DC, 652s.
  • [6] Nadenau, M.J., Winkler, S., Alleysson, D., Kunt, M., 2000. Human vision models for perceptually optimized image processing–a review. Proceedings of the IEEE, 32.
  • [7] Kerr, D., Mcginnity, T.M., Coleman, S., Clogenson, M. 2015. A Biologically Inspired Spiking Model of Visual Processing for Image Feature Detection. Neurocomputing, 158, 268-280.
  • [8] Kandel, E. R., Schwartz, J. H., Jessell, T. M. 2000. Principles of Neural Science. 4nd edition, McGraw-Hill, New York, 1760s.
  • [9] Hosoya, T., Baccus, S. A., Meister, M. 2005. Dynamic Predictive Coding by the Retina. Nature, 436, 71 – 77.
  • [10] Wu, Q., McGinnity, M., Maguire, L., Belatreche, A., Glackin, B., 2007, August. Edge detection based on spiking neural network model. In International Conference on Intelligent Computing (pp. 26-34). Springer, Berlin, Heidelberg.
  • [11] DiCarlo, J., Zoccolan, D., Rust, N.C. 2012. How does the Brain Solve Visual Object Recognition? Neuron 73(3), 415–434.
  • [12] Clarke, A., Tyler, L.K., 2015. Understanding what we see: how we derive meaning from vision. Trends in cognitive sciences, 19(11), 677-687.
  • [13] Ghahari, A., Enderle, J. D. 2015 Models of Horizontal Eye Movements: Part4, A Multiscale Neuron and Muscle Fiber-Based Linear Saccade Model. Synthesis Lectures on Biomedical Engineering, Morgan & Claypool Publishers.
  • [14] Kunkle, D. R., Merrigan, C. 2002. Pulsed Neural Networks and Their Application. Computer Science Dept., College of Computing and Information Sciences, Rochester Institute of Technology.
  • [15] Ghosh-Dastidar, S., Adeli, H., 2009. Spiking neural networks. International journal of neural systems, 19(04), 295-308.
  • [16] Ponulak, F. and Kasinski, A., 2011. Introduction to spiking neural networks: Information processing, learning and applications. Acta neurobiologiae experimentalis, 71(4), 409-433.
  • [17] Rozenberg, G., Bäck, T., Kok, J. N. 2011. Handbook of Natural Computing. Springer, Berlin, 2052s.
  • [18] Yedjour, H., Meftah, B., Lézoray, O., Benyettou, A., 2017. Edge detection based on Hodgkin–Huxley neuron model simulation. Cognitive processing, 18(3), 315-323.
  • [19] Wu, Q., McGinnity, M., Maguire, L., Glackin, B., Belatreche, A., 2007. Learning mechanisms in networks of spiking neurons. In Trends in Neural Computation (pp. 171-197). Springer, Berlin, Heidelberg.
  • [20] Meftah, B., Lezoray, O., Benyettou, A., 2010. Segmentation and edge detection based on spiking neural network model. Neural Processing Letters, 32(2), 131-146.
  • [21] Kerr, D., Coleman, S., McGinnity, M., Wu, Q. X., Clogenson, M. 2011. Biologically Inspired Edge Detection. 11th International Conference on Intelligent Systems Design and Applications, 22- 24 November, Cordoba, Spain.
  • [22] Díaz-Pernas, F.J., Antón-Rodríguez, M., de la Torre-Díez, I., Martínez-Zarzuela, M., GonzálezOrtega, D., Boto-Giralda, D., Díez-Higuera, J.F., 2011. Surround suppression and recurrent interactions V1–V2 for natural scene boundary detection. Image segmentation. INTECH Publisher, pp.99-118.
  • [23] Azzopardi, G., Petkov, N., 2012. A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model. Biological cybernetics, 106(3), 177-189.
  • [24] Hodgkin, A.L., Huxley, A.F., 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology, 117(4), 500- 544.
  • [25] Nelson, M. E., 2004. Electrophysiological Models. Koslow, S., Subramaniam, S., (Eds.) In Data Basing the Brain: From Data To Knowledge. Wiley, New York, 480s.
  • [26] FitzHugh, R. 1969. Mathematical Models of Excitation and Propagation in Nerve. McGraw Hill, New York.
  • [27] Nagumo, J., Sato, S. 1972. On a Response Characteristic of Mathematical Neuron Model. Kybernetik, 10(3), 155-164.
  • [28] Gerstner, W., Kistler, W. M. 2002. Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge Univ. Press, United Kingdom, 496s.
  • [29] Izhikevich, E. M. 2003. Simple Model of Spiking Neurons. IEEE Trans. Neural Networks, 14, 1569–1572.
  • [30] Maass, W., Bishop, C. M. 1999. Pulsed Neural Networks. MIT Press, Cambridge, MA, 377s.
  • [31] Richardson, M. J. E., Gerstner, W. 2003. Conductance Versus Current-Based Integrateand-Fire Neurons: Is There Qualitatively New Behaviour? Lausanne lecture.
  • [32] Mainen, Z. F. 1995. Mechanisms of spike generation in neocortical neurons. University of California, Doctoral dissertation, 72s, San Diego.
  • [33] Destexhe, A., 1997. Conductance-based integrate-and-fire models. Neural Computation, 9(3), 503-514.
  • [34] Koch, C. 1999. Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, New York, 588s.
  • [35] Dayan, P., Abbott, L. F. 2001. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, Cambridge, 480s.
  • [36] Müller, E. 2003. Simulation of high-conductance states in cortical neural networks. University of Heidelberg, Master’s Thesis, Germany, 41s.
APA INCETAS M, Uzun Arslan R (2019). Edge Detection Using Integrate and Fire Neuron Model. , 611 - 616. DOI: 10.19113/sdufenbed.570597
Chicago INCETAS MÜRSEL OZAN,Uzun Arslan Rukiye Edge Detection Using Integrate and Fire Neuron Model. (2019): 611 - 616. DOI: 10.19113/sdufenbed.570597
MLA INCETAS MÜRSEL OZAN,Uzun Arslan Rukiye Edge Detection Using Integrate and Fire Neuron Model. , 2019, ss.611 - 616. DOI: 10.19113/sdufenbed.570597
AMA INCETAS M,Uzun Arslan R Edge Detection Using Integrate and Fire Neuron Model. . 2019; 611 - 616. DOI: 10.19113/sdufenbed.570597
Vancouver INCETAS M,Uzun Arslan R Edge Detection Using Integrate and Fire Neuron Model. . 2019; 611 - 616. DOI: 10.19113/sdufenbed.570597
IEEE INCETAS M,Uzun Arslan R "Edge Detection Using Integrate and Fire Neuron Model." , ss.611 - 616, 2019. DOI: 10.19113/sdufenbed.570597
ISNAD INCETAS, MÜRSEL OZAN - Uzun Arslan, Rukiye. "Edge Detection Using Integrate and Fire Neuron Model". (2019), 611-616. https://doi.org/DOI: 10.19113/sdufenbed.570597
APA INCETAS M, Uzun Arslan R (2019). Edge Detection Using Integrate and Fire Neuron Model. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23(2), 611 - 616. DOI: 10.19113/sdufenbed.570597
Chicago INCETAS MÜRSEL OZAN,Uzun Arslan Rukiye Edge Detection Using Integrate and Fire Neuron Model. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23, no.2 (2019): 611 - 616. DOI: 10.19113/sdufenbed.570597
MLA INCETAS MÜRSEL OZAN,Uzun Arslan Rukiye Edge Detection Using Integrate and Fire Neuron Model. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol.23, no.2, 2019, ss.611 - 616. DOI: 10.19113/sdufenbed.570597
AMA INCETAS M,Uzun Arslan R Edge Detection Using Integrate and Fire Neuron Model. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2019; 23(2): 611 - 616. DOI: 10.19113/sdufenbed.570597
Vancouver INCETAS M,Uzun Arslan R Edge Detection Using Integrate and Fire Neuron Model. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2019; 23(2): 611 - 616. DOI: 10.19113/sdufenbed.570597
IEEE INCETAS M,Uzun Arslan R "Edge Detection Using Integrate and Fire Neuron Model." Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23, ss.611 - 616, 2019. DOI: 10.19113/sdufenbed.570597
ISNAD INCETAS, MÜRSEL OZAN - Uzun Arslan, Rukiye. "Edge Detection Using Integrate and Fire Neuron Model". Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23/2 (2019), 611-616. https://doi.org/DOI: 10.19113/sdufenbed.570597