18.3 Image Processing
Figure 18.3 Image Processing.
18.3.1 Edge Filters
Edge Filters extract and enhance edges & contours in an image by expressing intensity differences (gradients) between neighboring pixels as an intensity value. The basic variables are the differences between the top and bottom rows, the differences between the left and right columns, and the differences between the center point and its neighbors.
Edge Filters have the following selections:
Code Meaning
0 No Edge Filter
1 Sobel 1 (Prewitt)
2 Sobel 2 (Sobel)
3 Sobel 3
4 Sobel 4
5 Gradient
6 Gradient, 45°
7 Sobel 1, 45°
8 Sobel 1, - 45°
9 Laplacian 4
10 CD 11
11 FD 11
12 FD 9
13 FD 7
14 FD 13
15 Laplacian 5
16 Laplacian 8
17 Laplacian 9
18 Laplacian 16
19 Laplacian 17
All other filters have to be ordered in a Customized Version.
These names really do not make any sense to common users; the best way to figure out what these filters are, is to select a training image and try each of the filters. In general, these filters require the “Dark Background 128” Threshold Filter.
If you do not want to know the details, please skip the rest of this section.
The details will be given below so you will know how to order a customized filter:
Sobel 1:
-1 0 1 -1 -1 -1
-1 0 1 0 0 0
-1 0 1 1 1 1
Sobel 2:
-1 0 1 -1 -2 -1
-2 0 2 0 0 0
-1 0 1 1 2 1
Sobel 3:
-1 0 1 -1 -3 -1
-3 0 3 0 0 0
-1 0 1 1 3 1
Sobel 4:
-1 0 1 -1 -4 -1
-4 0 4 0 0 0
-1 0 1 1 4 1
Gradient:
0 0 0 0 -1 0
-1 0 1 0 0 0
0 0 0 0 1 0
Gradient, 45°
0 0 1 -1 0 0
0 0 0 0 0 0
-1 0 0 0 0 1
Sobel 1, 45°
0 1 1 1 1 0
-1 0 1 1 0 -1
-1 -1 0 0 -1 -1
Sobel 2, 45°
0 1 2 2 1 0
-1 0 1 1 0 -1
-2 -1 0 0 -1 -2
Laplacian 4
0 -1 0
-1 4 -1
0 -1 0
Laplacian 5
0 -1 0
-1 5 -1
0 -1 0
Laplacian 8
-1 -1 -1
-1 8 -1
-1 -1 -1
Laplacian 9
-1 -1 -1
-1 9 -1
-1 -1 -1
Laplacian 16
0 0 -1 0 0
0 -1 -2 -1 0
-1 -2 16 -2 -1
0 -1 -2 -1 0
0 0 -1 0 0
Laplacian 17
0 0 -1 0 0
0 -1 -2 -1 0
-1 -2 17 -2 -1
0 -1 -2 -1 0
0 0 -1 0 0
18.3.2 Threshold Filters
After Edge Filters, the Threshold Filter will be applied to the images. Choose these two filters where the sample objects stand out, otherwise change the filters.
If no filter in this version fits your problem, a Customized Filter has to be built. DO NOT make too many things stand out, i.e. as long as the area of interest stands out, the rest should show as little as possible.
Once you make a selection, the objects in the images are black and the background is white (like a book: white paper, black print). You should make the black area as small as possible, as long as it covers the key-segment(s). Otherwise, switch to a different background.
Figure 18.4 Threshold Filter Parameters.
There are 30 Threshold filters in the ImageFinder.
A few filters, including the average-filter and the customized-filter, allow you to specify any color range. Color is specified by three separate colors: Color = (red, green, blue). Each of the colors ranges from 0 to 255. (0, 0, 0) is black; (255, 255, 255) is white.
You should choose a filter where the sample object(s) stand out. You may want to know the meaning of the filters; example, "Light Background 128" means:
-
“RGB Average in 0 – 127 “ è objects; and
-
“RGB Average in 128 - 255“ è background.
To Summarize:
-
Choose an Edge Filter and a Threshold Filter where the sample object(s) stand out;
-
Choose an Edge Filter and a Threshold Filter where the black area is as small as possible, as long as it covers the key-segment(s).
18.3.3 Clean-Up Filters
Clean-Up Filters will clear noise off the image, but it will take more computation time.
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