Region Selection in .NET

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3. One more way is to determine the maximum value of the noise in the background, nmax , as the maximum difference between the pixels in that region and their mean, and preserve just those pixels that satisfy the condition (with f3 being yet another factor that can be varied as a function of the scenario): v mback > f3 nmax (17.7)
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Our tests have shown that all three methods lead to similar results. In the following, we use the second one, and the value of f2 is chosen to be 3 as a compromise between being able to preserve responses of shallowly buried targets without increasing significantly the clutter in the case of surface-laid objects.
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3.2 GPR Regions
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A simple means of region selection is to threshold projected A-scans. This gives good results, even without TVG [19]. Still, a potential general problem introduced by this method is that deeper buried objects have weaker signals and, accordingly, weaker energies. Therefore, it can happen that they are not detected if the threshold is not low enough, while a threshold that is too low leads to an increase in the number of false alarms. Even if TVG is applied, such a simple thresholding can rarely work well enough in case of humanitarian mine detection, which demands the highest possible detection rates. As shown in the previous section, the problem of correctly choosing the appropriate TVG is subtle, and strongly depends on a variety of factors. Consequently, it is practically impossible to be sure the chosen TVG is the right one. In order to preserve weaker signals, possibly belonging to deeper buried objects, without causing a strong increase in the number of false alarms, we propose a simple method for the region selection. The idea is to find local maxima and analyze their neighborhoods by grouping together all the points within some window around each of them, the value of which is close to the value of that local maximum. As a result, we get a blob within a window around every local maximum. Note that a blob belonging to one local maximum actually can be a group of blobs, and not just one connected blob. Namely, within a window around a local maximum there could be some regions with higher, and some with lower, energies than the chosen threshold (on percentage of the maximum decrease of a local maximum value) allows. This means the local maxima method needs three pieces of information as input. The first one is the size of the window around the local maxima within which the points are analyzed. Again (see the previous section), it must be larger than the size of the largest expectable object convolved with the GPR antenna opening and smaller than the minimum expectable distance between two objects in a lane. If this interval does not exist, mistakes can hardly be avoided either an object that is too large could be detected twice, or a few closely placed objects could be detected as a single object. A way to deal with this problem is mentioned in Section 7. A second input is the minimum value of local maxima that should still be detected (thr1 ). Obviously, this value affects the number of detected regions. Finally, the minimum percentage of the local maximum value so that neighboring pixels are grouped with it thr2 has to be chosen too. The choice of this value should not be critical, since it should influence sizes of all obtained blobs similarly.
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3.3 MD Regions
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Depth estimation through deconvolution, discussed in the previous section, asks for good quality images, i.e. with a good resolution. Thus, this method is feasible in cases of small images of single targets, and that diminishes its usefulness for the depth estimation in cases of region selection on images of broader areas. To our knowledge, there is no other way for determining the depth of an object in case of imaging MD data. Since the strength of the response of a metallic object depends on its depth,
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