We introduce the future work in the following topics. According to the research of the GPR images, we still have more discussions in research following the data complexity
increased. Focus on our proposed methods, we’ll discuss in three directions: first, other noises and hyperbolas and hyperbola detection; second, automatically; third, the 3-D model and the user interface.
Due to the non-even underground in Taiwan, there are more noises in the probed data.
There are no deeply discussed in the noises of pipes images. We hope to discuss and research fitten denoisy methods coordinated with the pipes images database. We only prove the outlook here. On the other hand, in hyperbolas, beside the complete hyperbolas, there still have other types of hyperbolas, one of them is the type of cross hyperbolas that are more important sin our application. In Fig. 5- 1, we show the example of this case, the cross
hyperbolas is not the complete hyperbola. Our proposed morphological hit-or-miss transform method can detection this case, but we’ll alter the parabola model. We show the simple model in Fig. 5- 2, we only add one coordinate
!
(x2, y2) to plot the non-complete parabola line.
Fig. 5- 1 The different situation of hyperbolas
Peak
!
(x1, y1)
!
(x2, y2)
Line1
Fig. 5- 2 The altered parabola model
Type II: Automatically
In the thesis, we manually create the threshold value and the parabola model coordinates.
However, when we have great amount of data, we’ll automatically process them for bring big benefit. In the following, we’ll describe some concepts. Our experiment shows if there are different strength hyperbolas in an image, it is hard using a single threshold value to hold the features of hyperbolas. When the complexity increases, we can use the multilevel threholding to create images class in detail. On the other hand in parabola model, due to fact that a high brightness hyperbola and a low brightness hyperbola, their thickness is also closer, as shown in Fig. 5- 3. When we obtain the locations of hyperbola (just like(1), something it shift to other location), we can regard the red curve line in the middle as the edge between the hyperbola with high brightness and the one with low brightness. When we move along the curve line, we’ll obtain the coordinate (1) in the max slope of the red curve line (Fig. 5- 3(b)).
According to the two different brightness hyperbola’s thickness is closer, and the areas of from (1) to (2) and from (1) to (3) are low frequency, we can set the (1) is the center and measure the coordinates of (2) and (3). In the edge disappear location, we can define the coordinate of (4) and (5). The great amount of data processed automatically will reduce the cost for us.
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(a) (b)
Fig. 5- 3The illustration of the parabola model creating automatically (a) The real image, (b) The schema of (a)
Type III: The 3-D Model and The User Interface
We show the GRP images can be shown with the pseudo-color or 3-D image technique beside the 8-bit gray value images. Different with the original images, the two methods will burden more cost. However, we have the benefit in more diversification showing. We believe that only the higher complexity GPR images can be considered using the above two methods.
We will show the examples about pseudo-color and 3-D images in our data in Fig. 5- 4 and Fig. 5- 5. Thus, we believe that design a useful and multi-function software is necessary for exporter and researcher. The other important development is the 2-D images translated to the 3-D model. In Fig. 5- 6, when we probe the pipes and draw three detection lines, we’ll obtain three section drawings. We plot the red curve lines as the hyperbola for example. An
important technique is to use many 2-D GPR images to translate to the 3-D object model. The research about this is widely discussed. Through our proposed method to enhance pipes images, we can predict the locations of pipes images and build the 3-D model more easily. To detect the location of pipes images and build more complete database is the future focal point to be discussed.
(1) (2)
(3)
(4)
(5)
Maximal curvalture
Fig. 5- 4 The pseudo-color of the Image1
Fig. 5- 5 The example of 3-D image of Image2
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Fig. 5- 6 The illustration of 3-D model of the pips images.
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