49843608-Complete-thesis-Report-merged

The rule viewer displays a roadmap of the whole fuzzy

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The Rule Viewer displays a roadmap of the whole fuzzy inference process. 4.6 The Surface Viewer Figure 4.6 The Surface Viewer.
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39 Upon opening the Surface Viewer, we are presented with a two-dimensional curve that represents the mapping from service quality to tip amount. Since this is a one-input one- output case, we can see the entire mapping in one plot. Two- input one-output systems also work well, as they generate three-dimensional plots that MATLAB can adeptly manage. When we move beyond three dimensions overall, we start to encounter trouble displaying the results. Accordingly, the Surface Viewer is equipped with pop-up menus that let you select any two inputs and any one output for plotting. Just below the pop-up menus are two text input fields that let you determine how many x-axis and y-axis grid lines you want to include. This allows you to keep the calculation time reasonable for complex problems. To change the x-axis or y-axis grid after the surface is in view, simply change the appropriate text field, and click on either X-grids or Y-grids, according to which text field you changed, to redraw the plot. 4.7 Algorithm to enhance image using fuzzy technique. The algorithm starts with the initialization of the image parameters; size, minimum, mid and maximum gray level. The fuzzy rule-based approach is a powerful and universal method for many tasks in the image processing. The algorithm is described as bellow: Step -1: Morphological processing i) Read the image ii) Convert it into Grayscale image if it is RGB image. iii) Find the size of the image (M X N). iv) Add the noise to the image (impulse noise). v) Find the minimum, maximum gray level of the image also find the average gray level of the image.
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40 Step -2: Convert the image data into Fuzzy domain data i) For x=0:M For y=0: N a) If gray_ value between zero and min Then fdata=0; b) Else if gray_ value between min and mid Then fdata = (1/(mid-min) * min + (1/mid- min)* data; c) If gray _ value between mid and max Then fdata = (1/(max-mid) )* mid + (1/(max-mid))*data; d) if gray_ level between max and 255 Then fdata=1; Step -3 Membership Modifications For x=0: M For y=0: N a) If gray _ value between 0 and min fdata= 0; // if the pixel is dark then make it darker. b) If gray _ value between min and mid. / / If the pixel gray then make it gray for x=0:3 i) If fdata between 0 and 0.5 Then fdata=2*(fdata) ^2; ii) Else if fdata between 0.5 and 1 Then fdata= 1-2*(1-fdata) ^2; c) If gray _ value between mid and max // if the pixel is bright then make it brighter
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41 for x=0; 3 i) If fdata between 0 and 0.5 Then fdata=2*(fdata) ^2; ii) If fdata between 0.5 and 1 Then fdata= 1- 2* (1-fdata) ^2; iii) If gray_ value between max and 255 Then fdata = 1; Step -4 Deffuzification For x=0: M For y=0: N a) If gray _ value between 0 and min Then enhanced_ data = gray _ value b) If gray_ value between min and mid Then enhanced_ data=-(mid min) * fdata+ min c) If gray _ value between mid and max Then enhanced_data= ( max mid) * fdata+mid; Step -5 displaying the Enhanced image.
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