Fuzzy Filtering for Two Dimensional Images of Mechanical Parts.
Alshnnaway, A., Aly, A. (2020). Fuzzy Filtering for Two Dimensional Images of Mechanical Parts.. EKB Journal Management System, 33(3), 1-7. doi: 10.21608/bfemu.2020.127512
Abdallah Alshnnaway; Ayman A. Aly. "Fuzzy Filtering for Two Dimensional Images of Mechanical Parts.". EKB Journal Management System, 33, 3, 2020, 1-7. doi: 10.21608/bfemu.2020.127512
Alshnnaway, A., Aly, A. (2020). 'Fuzzy Filtering for Two Dimensional Images of Mechanical Parts.', EKB Journal Management System, 33(3), pp. 1-7. doi: 10.21608/bfemu.2020.127512
Alshnnaway, A., Aly, A. Fuzzy Filtering for Two Dimensional Images of Mechanical Parts.. EKB Journal Management System, 2020; 33(3): 1-7. doi: 10.21608/bfemu.2020.127512

Fuzzy Filtering for Two Dimensional Images of Mechanical Parts.

MEJ- Mansoura Engineering Journal
Article 7, Volume 33, Issue 3, September 2008, Page 1-7  XML PDF (1.44 MB)
Document Type: Research Studies
DOI: 10.21608/bfemu.2020.127512
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Authors
Abdallah Alshnnaway email 1; Ayman A. Aly2
1Assistant Professor., Production Engineering and Mechanical Design Department., Faculty of Engineering., Tanta University., Tanta., Egypt.
2Assistant Professor., Production Engineering and Mechanical Design Engineering Department., Faculty of Engineering., Tanta University., Tanta., Egypt.
Abstract
The general idea behind the filter is to average a pixel using other pixel values from its neighborhood,  but simultaneously to take care of important image structures such as edges. The main concern of the  proposed filter is to distinguish between any variations of the captured digital image due to noise and  due to image structure. 
In this work a new fuzzy filler is presented for the noise reduction of images corrupted with additive  noise . The filter consists of three stages. (1) Define fuzzy sets in the input space to computes a fuzzy  derivative for eight different directions (2) construct a set of IF·THEN rules by 10 perform fuzzy  smoothing according to contributions of neighboring pixel values and (3) define fuzzy sets in the  output space to get the fiItered and edged image.  The edges give the image the appearance depth and sharpness. A loss of edg.es makes the image  appear blurred or unfocused. However. noise smoothing and edge enhancement are traditionally  conflicting tasks. Since most noise filtering behaves like a low pass filter, the blurring of edges and  loss of detail seems a natural consequence. Techniques to remedy this inherent connect of often encompass generation of new noise due to enhancement. Experimental results are obtained to show the feasibility of the proposed approach with two  dimensional objects. 
Keywords
Additive noise; Edge preserving filtering; Fuzzy image filtering; Noise reduction; Two dimensional mechanical images
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