HDevelop User's Manual

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98 CHAPTER 5. PROGRAM EXAMPLES 5.3 Particles File name: ÔÖØÐºÚ This program example processes an image that was taken from a medical application. It shows tissue particles on a carrier (figure 5.5 left). Figure 5.5: Tissue particles (left) and large objects (right). As in many other medical applications, the existing objects have to be evaluated statistically. This means that different objects have to be extracted and classified according to their size or other attributes for example. After this, you can analyze them. An important step to solve this problem is the image segmentation that locates the relevant objects. For the statistical evaluation you may have a look at appropriate literature about statistics. In our case there are two object classes: ¯ large, bright particles ¯ small, dark particles The large, bright particles differ clearly from the background because of their gray values. The informal description ’brighter than the background’ leads directly to the algorithmic solution using a thresholding. The only thing to decide is whether you specify the threshold automatically or empirically. In our case, a fixed threshold is completely sufficient due to the good contrast. Hence you get the following simple segmentation operator: ÖÑ ´ÈÖØÐ¸ ³ÔÖØÐ³µ ØÖ×ÓÐ ´ÈÖØÐ¸ ÄÖ¸ ½½¼¸ ¾µ The variable ÄÖ contains all pixels whose gray values are brighter than 110. You can see the result on the right side of figure 5.5. It is more difficult to find the small, dark particles. A first effort to specify a threshold interactively shows that there is no fixed threshold suitable to extract all particles. But if you look closer at the image you will notice that the smaller particles are much brighter than their local environment, i.e., you may specify suitable threshold values that are valid for a small image part <strong>HDevelop</strong> / 2000-11-16
5.3. PARTICLES 99 each. Now it is easy to transform this observation into an algorithm. One way is to determine the threshold values locally (e.g., from a bar chart). Another solution might be the definition of a local environment by an Ò ¢ Ò window. This method is used in the example. The window’s mean value is used as an approximation of the background intensity. This can be done by applying a low pass filter, such as a mean filter or a Gaussian filter. The window size Ò defines the size of the local environment and should approximately be twice as large as the objects to search for. Since they show an average diameter of 15 pixels, a mask size of 31 is used. The resulting pixels are specified by the comparison of the original gray values with the mean image. To reduce problems caused by noise you add a constant to the mean image (3). The appropriate program segment looks as follows: ÑÒÑ ´ÈÖØÐ¸ ÅÒ¸ ¿½¸ ¿½µ ÝÒØÖ×ÓÐ ´ÈÖØÐ¸ ÅÒ¸ ËÑÐÐ¸ ¿¸ ³ÐØ³µ The operator ÝÒ ØÖ×ÓÐ compares two images pixel by pixel. You can see the segmentation result in figure 5.6 left. Figure 5.6: Small objects: simple (left) and advanced segmentation (right). As we see, all objects have been found. Unfortunately, the edges of the large particles and several very small regions that emerged due to the noisy image material were found, too. We first try to suppress the edges. One way is to eliminate all objects that exceed a certain maximum size. You can do this by calling: ÓÒÒØÓÒ ´ËÑÐÐ¸ ËÑÐÐËÒÐµ ×ÐØ×Ô ´ËÑÐÐËÒÐ¸ ÊÐÐÝËÑÐÐ¸ ³Ö³¸ ³Ò³¸ ½¸ ¿¼¼µ By the same method you might also eliminate all objects which are too small (blurring). For this, you would just have to increase the minimum size with the call of ×ÐØ ×Ô. Butif you examine the segmentation results again, you will notice that some of the resulting pixels were already extracted by the first segmentation. Thus you should search the small particles within the complement of the large ones only. To avoid the segmentation of small particles in the direct neighbourhood of the large ones, those are enlarged before building their complement. Thus we get the following modified program: HALCON 6.0
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HALCON Version 6.0 MVTec Software G
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About This Manual This manual is a
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Contents 1 Introducing HDevelop 1 1
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Chapter 1 Introducing HDevelop In f
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1.2. EXAMPLE SESSION 3 Figure 1.1:
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Chapter 2 Graphical User Interface
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2.3. MAIN WINDOW 39 output paramete
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2.3. MAIN WINDOW 43 ËÙ×ØÓÒ×
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2.3. MAIN WINDOW 45 ÏÒÓÛ º ×
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30 APPENDIX B. DEVELOP Ú ×Ø ÛÒ
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32 APPENDIX B. DEVELOP Possible Suc
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34 APPENDIX B. DEVELOP See Also Ú
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36 APPENDIX B. DEVELOP HDevelop / 2
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38 APPENDIX C. GLOSSARY Operator wi
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40 Index Ú ×Ø ÔÖØ, 27, 39 Ú
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42 Index Operator Data base, 37 Nam
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44 Index HDevelop / 2000-11-16
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