Fuzzy Approach for Enhanced Edge Detection Algorithm by Entropy Optimization

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Dr. Gurpreet Kaur
Dr. Gurpreet Kaur
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Varun Raj
Varun Raj
α Guru Gobind Singh Indraprastha University Guru Gobind Singh Indraprastha University

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Fuzzy Approach for Enhanced Edge Detection Algorithm by Entropy Optimization

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Abstract

In this paper fuzzy based canny edge detection is explained. Global contrast intensification and local fuzzy edge detection are the two phases explained and is then merged with Canny operator for better results specially for noisy images and low contrast images. The resultant images are obtained using MATLAB which is the most convenient software and is efficient in terms of Image Processing as it is one of its toolbox. Although first-order linear filters constitute the algorithms most widely applied to edge detection in digital images but they don’t allow good results to be obtained where the contrast varies a lot, due to non-uniform lighting, as it happens during acquisition of most part of natural images.

References

10 Cites in Article
  1. John Canny (1986). A Computational Approach to Edge Detection.
  2. D Marr,E Hildreth (1980). Theory of edge detection.
  3. Stephen Smith,J Brady (1997). SUSAN—A New Approach to Low Level Image Processing.
  4. I Bloch (1994). Fuzzy sets in image processing.
  5. K Ho,N Ohnishi FEDGE -Fuzzy edge detection by fuzzy categorization and classification of edges.
  6. M Hanmandlu,D Jha,R Sharma (2003). Color image enhancement using fuzzification.
  7. Zheru Chi,Hong Yan,Tuan Pham (1996). Fuzzy Algorithms.
  8. N Pal,S Pal (1991). Entropy: a new definition and its applications.
  9. Ter Haar Romeny,B (2002). Front-End Vision and Multi-Scale Image Analysis.
  10. Azriel Rosenfeld,Mark Thurston,Yung-Han Lee (1972). Edge and Curve Detection: Further Experiments.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. Gurpreet Kaur. 2012. \u201cFuzzy Approach for Enhanced Edge Detection Algorithm by Entropy Optimization\u201d. Global Journal of Research in Engineering - J: General Engineering GJRE-J Volume 12 (GJRE Volume 12 Issue J2): .

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Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Version of record

v1.2

Issue date

May 8, 2012

Language
en
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In this paper fuzzy based canny edge detection is explained. Global contrast intensification and local fuzzy edge detection are the two phases explained and is then merged with Canny operator for better results specially for noisy images and low contrast images. The resultant images are obtained using MATLAB which is the most convenient software and is efficient in terms of Image Processing as it is one of its toolbox. Although first-order linear filters constitute the algorithms most widely applied to edge detection in digital images but they don’t allow good results to be obtained where the contrast varies a lot, due to non-uniform lighting, as it happens during acquisition of most part of natural images.

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Fuzzy Approach for Enhanced Edge Detection Algorithm by Entropy Optimization

Dr. Gurpreet Kaur
Dr. Gurpreet Kaur Guru Gobind Singh Indraprastha University
Varun Raj
Varun Raj

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