Applications of Cellular Neural Networks for Shape from Shading Problem
The Cellular Neural Networks (CNN) model consist of many parallel analog processors computing in real time. CNN is nowadays a paradigm of cellular analog programmable multidimensional processor array with distributed local logic and memory. One desirable feature is that these processors are arranged in a two dimensional grid and have only local connections. This structure can be easily translated into a VLSI implementation, where the connections between the processors are determined by a cloning template. This template describes the strength of nearest-neighbour interconnections in the network. The focus of this paper is to present one new methodology to solve Shape from Shading problem using CNN. Some practical results are presented and briefly discussed, demonstrating the successful operation of the proposed algorithm.
KeywordsMarkov Random Field Cellular Neural Network Relaxation Algorithm Hopfield Network Hopfield Model
Unable to display preview. Download preview PDF.
- 3.Roska, T. and Vandewalle, J. Cellular Neural Networks. (John Wiley&Sons), (1993)Google Scholar
- 6.Wechsler, H. Computer Vision, Academic Press, Inc, (1990)Google Scholar
- 11.Horn, B.K.P. “Obtaining Shape from Shading Information”. In The Psychology of Computer Vision, Winston, P.H., (Ed.). New York, McGraw-Hill, (1975), 115–155Google Scholar
- 13.Horn, B.K.P. “Local Shading Analysis”, IEEE Trans. Pattern Anal. Machine Intelligence, Vol. PAMI-16, No. 2 (Mar 1984), 170–184Google Scholar
- 15.Tsai, P.S. and Shah, M. “Shape from Shading using Linear Approximation”, Research Report, University of Central Florida, (1995)Google Scholar
- 17.Grimson, W.E.L. From Images to Surfaces: A Computational Study of the Human Early Visual System, MIT Press, Cambridge, MA, (1981)Google Scholar
- 21.Bose, N.K. and Liang, P. Neural Network Fundamentals with Graphs, Algorithms and Applications, McGraw-Hill Series in Electrical and Computer Engineering, (1996)Google Scholar