Abstract
The facet model for image processing takes the observed pixel values to be a noisy discretized sampling of an underlying gray tone intensity surface that in each neighborhood of the image is simple. To process the image requires the estimation of this simple underlying gray tone intensity surface in each neighborhood of the image. Prewitt (1970), Haralick and Watson (1981), and Haralick (1980, 1982, 1983, 1984) all use a least squares estimation procedure. In this note we discuss a Bayesian approach to this estimation problem. The method makes full use of prior probabilities. In addition, it is robust in the sense that it is less sensitive to small numbers of pixel values that might deviate highly from the character of the other pixels in the neighborhood.
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References
Haralick, R. M. (1980), “Edge and region analysis for digital image data,” Comput. Graphics and Image Processing 12, pp. 113–129.
Haralick, R. M. (1982). M. (1982), “Zero-crossing of second directional derivative edge operator,” Proceedings of the SPIE Technical Symposium East, Arlington, Va., May 3–7, 1982, 336, p. 23.
Haralick, R. M. (1983), “Ridges and valleys on digital images,” Comput. Vision Graphics and Image Processing 22, pp. 28–38.
Haralick, R. M. (1984), “Digital step edges from zero-crossing of second directional derivative,” IEEE Trans. Pattern Analysis and Machine Intelligence PAMI-6, No. 1, pp. 58–68.
Haralick, R. M., and Layne Watson (1981), “A facet model for image data,” Comput. Graphics and Image Processing 15, pp. 113–129.
Mosteller, Frederick, and John Tukey (1977), Data Analysis and Regression, Addison-Wesley, Reading, Mass., pp. 356–358.
Prewitt, Judy (1970), “Object enhancement and extraction,” in Picture Processing and Psychopictorics, B. Lipkin and A. Rosenfeld, eds., Academic Press, New York, pp. 75–149.
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© 1987 D. Reidel Publishing Company
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Haralick, R.M. (1987). A Bayesian Approach to Robust Local Facet Estimation. In: Smith, C.R., Erickson, G.J. (eds) Maximum-Entropy and Bayesian Spectral Analysis and Estimation Problems. Fundamental Theories of Physics, vol 21. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-3961-5_6
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DOI: https://doi.org/10.1007/978-94-009-3961-5_6
Publisher Name: Springer, Dordrecht
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