Lighting the importance of these algorithms by

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lighting the importance of these algorithms by considering that currently the medical imaging is an area which still needs to be investigated. 3. FUZZY EDGE DETECTION We decided to use fuzzy logic for edge detection inspired by human reasoning and hoping to get a development similar to human system. 3.1 Proposed algorithm The processing of captured images involves multiple proce- dures, so it is necessary to extract important features that will be used to develop an application. In the particular case of a human- oid robot, the system is constantly obtaining images that can be used to carry a set of important tasks, namely a) determine the position of the ball on the field, b) define play spaces and c) locate itself and its opponents into the field. These tasks can be all per- formed by processing the captured images and are a key aspect to create strategies according to the current state of each element. One of processes that can be applied to image is edge extrac- tion. It represents an important step because it allows an initial distribution into the field. The image preprocessing is to convert the color image to grayscale, and considering that the edges are pixels that have a significant variation in gray level; it is possible to determine a chain of pixels representing the edges. For a refer-

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[38] Perez A. et al. / Edge Detection Algorithm Based on Fuzzy Logic Theory for a Local Vision System of Robocup Humanoid League Tecno Lógicas ence pixel I xy is the difference with neighboring elements by estab- lishing a matrix as shown in Fig. 1. I x-1,y-1 I x-1,y I x-1y+1 I x,y-1 I x,y I x,y+1 I x+1y-1 I x+1,y I x+1y+1 Fig. 1. Mask of pixels. Source: Authors In where an estimation of a difference or a variation between elements, was performed according to (1) to (8). (1) (2) (3) (4) (5) (6) (7) (8) To perform edge extraction using fuzzy logic theory is neces- sary to determine a fuzzy set to evaluate the degree of member- ship of each difference, called input membership function, μ _IN. Later it was determined another fuzzy set that assigns an output value, called the output membership function, μ _OUT. The equa- tion that describes the membership function of a fuzzy set X is explained below in (9) and (10).
Tecno. Lógicas., No. 30, enero-junio de 2013 [39] ( ) (9) { } (10) Where c represents the bell center, is the opening and b is slope for discourse universe Z. Fig. 2. Shows input membership function corresponding to variation of gray level. Fig. 2. Input membership function. Source: Authors The choice of rules is based on variation of gray level of a ref- erence pixel I xy with each of its neighbors, by defining the follow- ing rule base: IF d1 is Low AND d2 is Low THEN pixel I xy is white. Taking this as a base rule, we proceed to make a set of 8 rules with the conditions described in Table 1.

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[40] Perez A. et al. / Edge Detection Algorithm Based on Fuzzy Logic Theory for a Local Vision System of Robocup Humanoid League Tecno Lógicas Tabla 1. Set of rules. Source: Authors RULE CONDITION 1 CONDITION 2 i d(i) d(i+1) Where i is expressed in (11): (11) The output membership function shown in Fig. 3, which as- sesses the input membership degree. Thus a maximum variation
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• Spring '16
• jane
• fuzzy logic theory, edge detection algorithm

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