Thus a maximum variation is represented by black

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sesses the input membership degree. Thus a maximum variation is represented by black color or value 0 and the absence of varia- tion is represented by white color with value 255. Fig. 3. Output membership function. Source: Authors The evaluation of each rule in the output membership function generates one area, for a total of 8 sub-areas of which are neces- sary to determine a weighted. For these we calculate the maxi- mum between found areas, thus this will be the total area to de- fine and perform defuzzification process to a point value to facili- tate the decision process. This value is obtained by the method of defuzzification of bisector of area (BOA) described in (12).
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Tecno. Lógicas., No. 30, enero-junio de 2013 [41] (12) Where (13), (14) and Z BOA is a line that divides the region be- tween and in two parts with same area. { } (13) { } (14) For edges extraction, the output obtained of defuzzification process is an image that contains edges resulting from the pro- posed algorithm, which are in range of 0 to 255, where edges are close to 0. Therefore, it is necessary binarize the image through a threshold using (15), so that any value that is below the threshold φ is considered edge, otherwise the pixel is discarded. (15) The algorithm used to determine φ value that allows a proper selection of edges is Otsu, widely used for optimal selection of thresholds automatically (Gómez et al., 2010; Otsu, 1994). Consid- ering that the application of Otsu enables a dynamic selection, according to illumination conditions and threshold for binarize the image. At Fig.4 it is shown flowchart for described algorithm.
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[42] Perez A. et al. / Edge Detection Algorithm Based on Fuzzy Logic Theory for a Local Vision System of Robocup Humanoid League Tecno Lógicas Fig. 4. Flowchart of fuzzy edge detection. Source: Authors 4. RESULTS AND DISCUSSION We have designed a set of experiments in order to assess the performance of the proposed algorithm. In the experiments, a humanoid robot carries a camera and takes pictures of its envi- ronment. In general, each picture has different characteristics in terms of illumination, environment and external noise, which
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Tecno. Lógicas., No. 30, enero-junio de 2013 [43] allow s us to fully assess the algorithm’s capabilities when faced to a variety of conditions. The experiments run in an Intel Core i7 processor with 8GB RAM and 64-bit Windows operating system. In the first experiment we have selected an image with few sources of external noise to the field and clearly defined lines as we can see in Fig. 5(a). We have applied the fuzzy-based border extraction method to the picture to obtained the Fig. 5(b) with a pixel-value distribution of 0 255 as described above. This new image needs to be binarized in order to isolate and identify the borders. For this purpose, we have used the Otsu’s method mainly due to its capability to automatically select a threshold to binarize the image without having to manually analyze the particular illumi- nation conditions. This is shown in Fig. 5(c). We can see that the algorithm correctly extract the image borders, these are continu- ous and take 1.26 seconds overall.
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