Abstract
This paper examines the use of two characteristic neural network architectures to signal and image processing applications. Digital image halftoning and seismic event detection are treated as optimization problems, to which symmetric Hopfield-type networks with near-neighbor-connections provide efficient solutions. A solution to the halftoning problem, provided by simulated annealing, is also examined and compared to the neural network one. Feedforward multilayered networks are examined in the form of auto-associative memories, using the same input and desired output data. The ability of such networks to compress sequences of image frames, having been trained over a small number of them, is specifically examined in the paper. The performance of a network trained by the backpropagation learning algorithm is compared to that of a counterpropagation network applied to the same sequence of real images.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
J.J. Hopfield and D.W. Tank, “Neural Computation of Decisions in Optimization Problems”, Biological Cybernetics, vol. 52, pp. 141–152, 1985.
D.W. Tank and J.J. Hopfield, “Simple Neural Optimization Networks: An A/D Converter, Signal Decision Circuit and a Linear Programming Circuit”, IEEE Trans. Circuits and Systems, vol CAS-33, no 5, pp. 533–541, 1986.
D. Rumelhart, G. Hinton and G. Williams, “Learning Internal Representations by Error Propagation”, in “Parallel Distributed Processing”, vol.1, Eds. D. Rumelhart, J. McCleland, MIT Press, Cambridge MA, 1986.
J.F. Jarvis, C.N. Judice and W.J. Ninke, “A Survey of Techniques for the Display of Continuous-Tone Pictures on Bilevel Displays”, Computer Graphics and Image Processing, vol.5, pp. 13–40, 1976.
J.L. Mannos and D.J. Sakrison, “The Effects of a Visual Fidelity Criterion on the Encoding of Images”, IEEE Trans. Information Theory, vol. IT-20, pp. 526–536, 1974.
J. Mendel, “Optimal Seismic Deconvolution: An Estimation Based Approach”, New York, Academic, 1983.
S. Kollias and C. Halkias, “An Instrumental Variable Approach to Minimum Variance Deconvolution”, IEEE Trans. on Geosci. and Remote Sensing, vol. 23, pp. 778–788, 1985.
G. Cotrell, P. Munro and D. Zipser, “Image Compression by Backpropagation”, Technical Report, UCSD Inst. for Cognitive Sci., San Diego, USA, 1987.
S.Kollias and D.Anastassiou, “An Adaptive Least Squares Algorithm for the Efficient Training of Artificial Neural Networks”, IEEE Trans. on Circuits and Systems, vol.36, August 1989.
R. Hecht-Nielsen, “Counter Propagation Networks”, IEE 1st Intern. Conference on Neural Networks, San Diego, USA, June 1987.
D. Anastassiou, “Error Diffusion Coding for A/D Conversion”, IEEE Trans. on Circuits and Systems, vol. 36, pp. 1175–1186, 1989.
P. Carnevalli, L. Coletti and S. Patarnello, “Image Processing by Simulated Annealing”, IBM Journal for Research and Development, vol. 29, pp. 569–579, 1985.
G. Giannakis, J. Mendel and X. Zhao, “A Fast Prediction Error Detector for Estimating Sparse-Spike Sequences”, IEEE Trans. on Geosc. and Remote Sensing, vol. 27, pp. 344–351, 1989.
T. Kohonen, “Self Organization and Associative Memory”, Springer-Verlag, 1984.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1990 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kollias, S. (1990). A study of neural network applications to signal processing. In: Almeida, L.B., Wellekens, C.J. (eds) Neural Networks. EURASIP 1990. Lecture Notes in Computer Science, vol 412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-52255-7_44
Download citation
DOI: https://doi.org/10.1007/3-540-52255-7_44
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-52255-3
Online ISBN: 978-3-540-46939-1
eBook Packages: Springer Book Archive