Abstract
In this paper we present two algorithms for color image segmentation based on Huang's idea of describing the segmentation problem as the one of minimizing a suitable energy function for a Hopfield network. The first algorithm builds three different networks (one for each color feature) and then combine the results. The second builds a unique network according to the number of clusters obtained by histogram analysis. Experimental results, heuristically and quantitatively evaluated, are encouraging.
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J.J. Hopfield “Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79: 2554–2558 (1982).
J.J. Hopfield “Neurons with graded response have collective computational properties like those of two-states neurons”. Proc. Natl. Acad. Sci. USA 81: 3088–3092 (1984).
C-L. Huang “Parallel image segmentation using modified Hopfield model” Pattern Recognition Letters 13: 345–353 (1992).
A. Rosenfeld, A.C. Kak “Digital picture processing” Academic Press Inc, Orlando, Florida, 1982.
Y-H Yang, J. Liu “Multiresolution image segmentation” IEEE Trans. on Pattern Analysis and Machine Intelligence 16: 689–700 (1994).
Y. Takefuji “Neural network parallel computing”, Kluwer Academic Publishers, 1992.
A.P. Witkin “Scale-Space Filtering: A New Approach to Multi-scale Description” Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 3: 39A.1.1–39A.1.4 (1984).
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© 1995 Springer-Verlag Berlin Heidelberg
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Campadelli, P., Medici, D., Schettini, R. (1995). Using Hopfield networks to segment color images. In: Braccini, C., DeFloriani, L., Vernazza, G. (eds) Image Analysis and Processing. ICIAP 1995. Lecture Notes in Computer Science, vol 974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60298-4_232
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DOI: https://doi.org/10.1007/3-540-60298-4_232
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