Abstract
In this paper, we propose a spiking neural network model for edge detection in images. The proposed model is biologically inspired by the mechanisms employed by natural vision systems, more specifically by the biologically fulfilled function of simple cells of the human primary visual cortex that are selective for orientation. Several aspects are studied in this model according to three characteristics: feedforward spiking neural structure; conductance-based model of the Hodgkin–Huxley neuron and Gabor receptive fields structure. A visualized map is generated using the firing rate of neurons representing the orientation map of the visual cortex area. We have simulated the proposed model on different images. Successful computer simulation results are obtained. For comparison, we have chosen five methods for edge detection. We finally evaluate and compare the performances of our model toward contour detection using a public dataset of natural images with associated contour ground truths. Experimental results show the ability and high performance of the proposed network model.
References
Azzopardi G, Petkov N (2012) A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model. Biol Cybern 106(3):177–189
Chaturvedi S, Kurshid A (2015) ASIC implementation for improved character recognition and classification using SNN model. Procedia Comput Sci 62:151–158
Clark A, Tyler LK (2015) Understanding what we see: how we derive meaning from vision. Trends Cogn Sci 19(11):677–687
Clausi D, Ed Jernigan M (2000) Designing Gabor filters for optimal texture separability. Pattern Recogn 33:1835–1849
Daugman JG (1985) Uncertainty relations for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J Opt Soc Am 2:1160–1169
Destexhe A, Rudolph M, Fellous JM, Sejnowski T (2001) Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107:13–24
DiCarlo J, Zoccolan D, Rust NC (2012) How does the brain solve visual object recognition? Neuron 73(3):415–434
Diaz-Pernas FJ, Anton-Rodriguez M, Torre-Diez I, Martinez-Zarzuela M, Gonzalez-Ortega D, Boto-Giralda D, Diez-Higuera JF (2011) Surround suppression and recurrent interactions V1–V2 for natural scene boundary detection. In: Ho P-G (ed) Image segmentation. INTECH Publisher, pp 99–118
Friedrich J, Urbanczik R, Senn W (2014) Code-specific learning rules improve action selection by populations of spiking neurons. Int J Neural Syst 24(1):1–16
Ghahari A, Enderle JD (2015) Models of horizontal eye movements: Part4, a multiscale neuron and muscle fiber-based linear saccade model. Synthesis Lectures on Biomedical Engineering, 9(4). Morgan & Claypool Publishers
Ghosh DS, Adeli H (2009) Spiking neural networks. Int J Neural Syst 19(4):295–308
Hodgkin A, Huxley A (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500–544
Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160:106–154
Hubel DH, Wiesel TN (1963) Shape and arrangement of columns in cat’s striate cortex. J Physiol 165:559–568
Hubel DH, Wiesel TN (1977) Functional architecture of macaque monkey visual cortex. Proc R Soc Lond 198:1–59
Iakymchuk T, Rosado-Muñoz A, Guerrero-Martínez JF, Bataller-Mompen M, Francé-Vllora JV (2015) Simplified spiking neural network architecture and STDP learning algorithm applied to image classification. EURASIP J Image Video Process 2015(4):1–11
Jones JP, Palmer LA (1987) An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol 58(6):1233–1258
Kandel ER, Shwartz JH (1981) Principles of neural science. Edward Amold (Publishers) Ltd., London
Kerr D, Coleman S, McGinnity M, Wu QX, Clogenson M (2011) Biologically inspired edge detection. In: 11th international conference on intelligent systems design and applications, pp 65–66, 802–807
Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: 8th international conference computer vision, vol 2, pp 416–423
Maunsell JHR, Newsome WT (1987) Visual processing in monkey extrastriate cortex. Annu Rev Neurosci 10:363–401
Meftah B, Lezoray O, Benyettou A (2010) Segmentation and edge detection based on spiking neural network model. Neural Process Lett 32(2):131–146
Meftah B, Lézoray O, Chaturvedi S, Khurshid A, Benyettou A (2013) Image processing with spiking neuron networks. In: Yang XS (ed) Artificial intelligence, evolutionary computation and metaheuristics, SCI 427. Springer, New York, pp 525–544
Nelson ME (2004) Electrophysiological models. In: Koslow S, Subramaniam S (eds) Databasing the brain: from data to knowledge. Wiley, New York
Papari G, Campisi P, Petkov N, Neri A (2007) A biologically motivated multiresolution approach to contour detection. EURASIP J Adv Signal Process 2007:1–27
Ponulak F, Kasinski A (2011) Introduction to spiking neural networks: information processing, learning and applications. Acta Neurobiol Exp 71(4):409–433
Pratt WK (2007) Digital image processing, 4th edn. Wiley, New York
Rossello JL, Canals V, Oliver A, Alomar M, Morro A (2014) Spiking neural networks signal processing. In: Design of circuits and integrated systems, pp 1–6
Rozenberg G, Bck T, Kok JN (2011) Handbook of natural computing. Springer, Berlin
Ursino M, La Cara GE (2004) A model of contextual interactions and contour detection in primary visual cortex. Neural Netw 17(5–6):719–735
Wu QX, McGinnity TM, Maguire LP, Belatreche A, Glackin B (2007) Edge detection based on spiking neural network model. Adv Intell Comput Theor Appl Asp Artif Intell LNCS 4682:26–34
Yang S, Wu Q, Li R (2011) A case for spiking neural network simulation based on configurable multiple-FPGA systems. Cogn Neurodyn 5:301–309
Author information
Authors and Affiliations
Corresponding author
Additional information
Handling editor: Howard Bowman (University of Kent); Reviewer: George Parish (University of Kent).
Rights and permissions
About this article
Cite this article
Yedjour, H., Meftah, B., Lézoray, O. et al. Edge detection based on Hodgkin–Huxley neuron model simulation. Cogn Process 18, 315–323 (2017). https://doi.org/10.1007/s10339-017-0803-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10339-017-0803-z