Abstract
This paper introduces a method for low level image segmentation. Pixels of the image are classified corresponding to their chromatic features.
The classifier is implemented by means of a neural network trained using a manual clasification of the coloured images. The back-propagation algorithm is applied to one image which works as training set, and the results applied to the rest of them.
Because of the large amount of data contained in one image, a random selection of the points is made before training.
Finally, the paper shows the results of applying the method to fruits detection in natural scenes taken from the open field.
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References
Haralick, R. M. and Shapiro, L.G. "Image segmentation techniques". Computer Vision, Graphics and Image Processing, 29 (1985).
Ballard, D.N. and Brown, C.M. "Computer Vision". Prentice Hall, 1982
Wyszecky, G. and Stiles, V.S. "Color Science" John Wiley and Sons Inc, 1978
Pratt, W.K. "Digital Image Processing" John Wiley and Sons, Inc. 1978
Albert, J. Ferri, F., Domingo, J. and Vicens, M. "An approach to Natural Scene Segmentation by means of Genetic Algoritms with Fuzzy Data".
Lorentz, G.G. "The 13th. Problem of Hilbert" in F.E. Browder (Ed). Mathematical developments Arising from Hilbert Problems, American Mathematical Society, Providence, R.I. 1976.
Kohonen, T. "Self-Organization and Associative Memory". Springer-Verlag, Berlin, 1984 and 88
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© 1991 Springer-Verlag Berlin Heidelberg
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Vicens, M., Albert, J., Arnau, V. (1991). An application of neural networks to natural scene segmentation. In: Prieto, A. (eds) Artificial Neural Networks. IWANN 1991. Lecture Notes in Computer Science, vol 540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035911
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DOI: https://doi.org/10.1007/BFb0035911
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