By C. A. Glasbey
Utilizing a statistical point of view, this paintings introduces a couple of sensible examples from different different types of microscopy, scientific imaging platforms, distant sensing and phone prints to demonstate different variety of picture research functions. Explains the character of electronic pictures and terminology use. Reference can be made to extensions of multivariate and multi-temporal photographs. The appendix includes beneficial assistance on making a choice on hardware/software for a particular program.
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2. 2 3 Greyscale display Most common digital images — certainly most in this book — are greyscale. The natural way to display such images is to use the pixel values to specify the brightness with which a pixel is illuminated on a computer screen, or how bright the pixel appears on a printed page. 2, the pixel values are some measured physical property of the object being studied. If this property is the amount of reflected or transmitted light, then the display we produce will simply look like a black and white photograph of the object.
A linear filter of size (2m + 1) × (2m + 1), with specified weights wkl for k, l = −m, . . , m, gives gij = m m wkl fi+k,j+l for i, j = (m + 1), . . , (n − m). k=−m l=−m For example, if m = 1, then the window over which averaging is carried out is 3 × 3, and gij = w−1,−1 fi−1,j−1 + w0,−1 fi,j−1 + w1,−1 fi+1,j−1 + w−1,0 fi−1,j + w0,0 fi,j + w1,0 fi+1,j + w−1,1 fi−1,j+1 + w0,1 fi,j+1 + w1,1 fi+1,j+1 . For full generality, the weights (w) can depend on i and j, resulting in a filter which varies across the image.
N − m); j = 1, . . 1. 2: Linear smoothing filters applied to X-ray image: (a) 3 × 3 moving average, (b) Gaussian, σ 2 = 23 , (c) 5 × 5 moving average, (d) Gaussian, σ 2 = 2, (e) 9 × 9 moving average, (f ) Gaussian, σ 2 = 6 23 . CHAPTER 3. FILTERS 6 c c using column weights w−m , . . , wm , followed by a row operation: gij = m l=−m wlr hi,j+l for i, j = (m + 1), . . , (n − m), r r , . . , wm . In order for this to be possible, the array of weights, wkl , must using row weights w−m be expressible as the product of the column and row weights, as follows: wkl = wkc wlr for k, l = −m, .
