Lighting the importance of these algorithms by

Info icon This preview shows pages 5–9. Sign up to view the full content.

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-
Image of page 5

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

[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).
Image of page 6
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.
Image of page 7

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

[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
Image of page 8
Image of page 9
This is the end of the preview. Sign up to access the rest of the document.
  • Spring '16
  • jane
  • fuzzy logic theory, edge detection algorithm

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern