On edge detection based on new intuitionistic fuzzy divergence and entropy measures

Edges of the image plays an important role in the field of digital image processing and computer vision. It reduces the amount of data, extract useful information from the image and also preserve significant structural properties of an input image. Further, it can be used in object and facial expres...

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Vydáno v:	2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC) s. 689 - 693
Hlavní autoři:	Ansari, Mohd Dilshad, Mishra, Arunodaya Raj, Ansari, Farhina Tabassum, Chawla, Meenu
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 2016
Témata:	Detectors Digital images Edge detection Entropy Entropy measure Feature extraction Fuzzy sets Image edge detection Intuitionistic fuzzy divergence measure Intuitionistic fuzzy set
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Popis
Shrnutí:	Edges of the image plays an important role in the field of digital image processing and computer vision. It reduces the amount of data, extract useful information from the image and also preserve significant structural properties of an input image. Further, it can be used in object and facial expression detection. In this paper, we have proposed new intuitionistic fuzzy divergence and entropy measures with its proof of validity for an intuitionistic fuzzy sets. A new and significant technique has been developed for edge detection. The proposed method has been demonstrated on various sample images. The detected edges of the sample images are true, smooth and sharpen which is found to be better than existing methods.
DOI:	10.1109/PDGC.2016.7913210