Many of the topics already taken up have underlying implications of numerical evaluation, even where the treatment is presented in the form of continuous analysis. In some cases the computational evaluation is straightforward, as with many integrals, but in other cases ideas enter in that are associated with discrete mathematics. Many of these ideas come to the fore the present chapter, which begins with a variety of frequently needed elementary operations, such as smoothing and sharpening digital images, and continues on to introduce morphological operations, such as dilation and erosion, that are prerequisite to handling binary objects and to following the literature of feature recognition in images. Where it helps to clarify numerical procedures, short segments of computer pseudocode have been supplied, and the opportunity has been taken to point out how algebraic notation such as A ∪ B and A ⊕ B translate directly into the simple logical expressions acceptable to computers.
KeywordsRetina Autocorrelation Convolution Bors Azimuth
Unable to display preview. Download preview PDF.
- J. D Foley, A. van Dam, S. K. Feiner, AND J. F. Hughes (1990), Computer Graphics Principles and Practice, Addison-Wesley, Reading, MA.Google Scholar
- B. K. P. Horn AND M.J. Brooks (1989), Shape from Shading, MIT Press, Cambridge, MA.Google Scholar
- A. Lohman (1991), “Object recognition using image algebra,” Angewandte Optik, Annual Report, p. 41, Physikalisches Institutder Universität Erlangen, Nürnberg.Google Scholar
- W. K. Pratt (1978), Digital Image Processing, John Wiley, New York.Google Scholar
- A. Rosenfeld AND A. C. Kak (1976), Digital Picture Processing, Academic Press, New York.Google Scholar
- F. Y. Spih AND Hong Wu (1991), “Optimization on Euclidean distance transformation using grayscale morphology,” J. Visual Communication and Image Representation, vol. 3, pp. 104–114.Google Scholar
- R. L. Weber (1974), A Random Walk in Science, The Institute of Physics, London.Google Scholar