Edge Detection in Gray Level Images Based on Non-Shannon Entropy

Digital image processing is a subset of the electronic domain, wherein the image is converted to an array of small integers, called pixels, representing a physical quantity. Edges characterize boundaries and edge detection is one of the most difficult tasks in image processing hence it is a problem...

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Bibliographic Details
Published in:International journal on computer science and engineering 2013-12, Vol.5 (12), p.932-932
Main Author: El-Owny, Hassan Badry Mohamed A
Format: Article
Language:English
Subjects:
Arrays
Boundaries
Edge detection
Entropy
Image detection
Image processing
Production methods
Tasks
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Summary:Digital image processing is a subset of the electronic domain, wherein the image is converted to an array of small integers, called pixels, representing a physical quantity. Edges characterize boundaries and edge detection is one of the most difficult tasks in image processing hence it is a problem of fundamental importance in image processing. The problem of edge detection although it is fundamental and is existing since years but it is still an area where there is still scope of research. It has been found that the previous used algorithms or methods were not able to produce ideal or optimized results. This paper presents an efficient techniques based on Non-Shannon measures of entropy for edge detection. Our objective is to find the best edge representation. A set of experiments in the domain of edge detection are presented. The experimental results show that the new technique often yield more efficient results comparing with classic methods.
ISSN:0975-3397