Abstract
This paper presents a cellular neural network based edge detection using Raster CNN Simulator. The software is designed to handle with both gray level and color images. The experimental result of Raster CNN Simulator is compared with traditional edge detection operators Canny and Sobel. Simulation results show that the proposed simulator is accurately detecting the complete image edge and also save the computation time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chua, L.O., Yang, L.: Cellular Neural Networks: Theory. IEEE Transactions on Circuits and Systems 35, 1257–1272 (1988)
Chua, L.O., Yang, L.: Cellular Neural Networks: Applications. IEEE Transactions on Circuits and Systems 35, 1273–1290 (1988)
Chua, L.O.: A paradigm for complexity. World scientific Series on Nonlinear Science, Series A, vol. 31. World Scientific, Singapore (1998)
Chua, L.O., Roska, T.: The CNN paradigm. IEEE Transactions on Circuits and Systems – I: Fundamental Theory And Application 40, 147–156 (1993)
Chua, L.O., Roska, T., Kozek, T., Zarandy, A.: The CNN paradigm, cellular neural networks. John Wiley, Chichester (1993)
Yuksel, M.E.: Edge detection in noisy images by neuro-fuzzy processing. International Journal of Electronics and Communications 61, 82–89 (2007)
Yuksel, M.E., Yildirim, M.T.: A Simple Neuro-Fuzzy Edge Detector for Digital Images Corrupted by Impulse Noise. International Journal of Electronics and Communications 58, 72J–75J (2004)
Haberstroh, L., Kurz, L.: Line detection in noisy and structured background using graco-latin squares. CVGIP: Graphical Models Image Process 55, 161–179 (1993)
Hansen, F.R., Elliot, H.: Image segmentation using simple markov field models. Computer Graphics and Image Process 20, 101–132 (1982)
Huang, J.S., Tseng, D.H.: Statistical theory of edge detection. Computer Vision Graphics and Image Processing 43, 337–346 (1988)
Nahi, N.E., Assefi, T.: Bayesian recursive image estimation. IEEE Transactions on Computers 7, 734–738 (1972)
Stern, D., Kurz, L.: Edge detection in correlated noise using latin squares models. Pattern Recognition 21, 119–129 (1988)
Kirsch, R.A.: Computer determination of the constituent structure of biological images. Computers and Biomedical Research 4, 314–328 (1971)
Marr, D., Hidreth, E.: Theory of edge detection. Proceedings of the Royal Society London 207, 187–217 (1980)
Canny, A.: computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)
Murugesh, V.: Raster Cellular Neural Network Simulator for Image Processing Applications with Numerical Integration Algorithms. International Journal of Computer Mathematics 86, 1215–1221 (2009)
Murugesh, V.: Image Processing Applications via Time-Multiplexing Cellular Neural Network Simulator with Numerical Integration Algorithms. International Journal of Computer Mathematics 87, 840–848 (2010)
Lee, C.-C., de Gyvez, P.: Single-layer CNN simulator. In: International Symposium on Circuits and Systems, vol. 6, pp. 217–220 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Murugesh, V., Kim, KT. (2011). An Efficient Edge Detection Using Raster CNN Simulator. In: Lee, G., Howard, D., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2011. Lecture Notes in Computer Science, vol 6935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24082-9_77
Download citation
DOI: https://doi.org/10.1007/978-3-642-24082-9_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24081-2
Online ISBN: 978-3-642-24082-9
eBook Packages: Computer ScienceComputer Science (R0)