By Peter W. Hawkes (Ed.)
This quantity comprises chapters discussing picture enhancement and propagation, and box behaviour in hugely anisotropic media. it's meant for researchers in optical technological know-how, electric engineering, picture processing and mechanical engineering.
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Additional info for Advances in Imaging and Electron Physics, Vol. 92
This technique gives the user the possibility of solving complex image analysis of pattern selection and enhancement problems, by developing an opportune sequence of erosions, dilations, and point-to-point operations between images, and by choosing at each time the proper structuring element. , the fact that, in the general case, the dilation does represent the erosion’s inverse operation (and vice versa) only for certain patterns contained in the image, depending upon the structuring element’s shape and upon the image contents.
Y)). 4 The upper right of Fig. 6. d. Weighted Unsharp Masking (Jackson and Kaye, 1982). The conventional unsharp masking is a well-known method for enhancing high spatial frequencies by subtracting from the original image f ( x , y) a blurred version b(x,y) of the same. If b(x,y) is obtained by means of a linear low pass, as for instance by determining the unweighted average gray value inside of the observation window U,the unsharp masking is equivalent to a linear high pass. Now, let us consider an ideal gray-value step function with step height A , and an edge pixel p = (xo,yo).
Inside of each field F,,, 1 Ic, r IQ,the gray-value function f(x, y ) is approximated by a separate linear function - X c r ) + BcAY + Ccr, where A,,, B,,, and C,, are constants, and (X,,, I-&) is the center of Fcr. 5 - I&)’. Fcr In other words, one obtains the plane coefficients by convolving the image with three kernels, two consisting of the local coordinates, and the third being an averaging kernel. The enhanced image u(x,y) results from a weighted difference between the original image and the background: 1 U ( X , Y ) = -[for,u) - 1-k k * g(x, u)l.