LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

New Divergence and Entropy Measures for Intuitionistic Fuzzy Sets on Edge Detection

Photo by timothycdykes from unsplash

Edges of the image play an important role in the field of digital image processing and computer vision. The edges reduce the amount of data, extract useful information from the… Click to show full abstract

Edges of the image play an important role in the field of digital image processing and computer vision. The edges reduce the amount of data, extract useful information from the image and preserve significant structural properties of an input image. Further, these edges can be used for object and facial expression detection. In this paper, we will propose new intuitionistic fuzzy divergence and entropy measures with its proof of validity for intuitionistic fuzzy sets. A new and significant technique has been developed for edge detection. To check the robustness of the proposed method, obtained results are compared with Canny, Sobel and Chaira methods. Finally, mean square error (MSE) and peak signal-to-noise ratio (PSNR) have been calculated and PSNR values of proposed method are always equal or greater than the PSNR values of existing methods. The detected edges of the various sample images are found to be true, smooth and sharpen.

Keywords: divergence entropy; detection; intuitionistic fuzzy; edge detection; fuzzy sets; entropy measures

Journal Title: International Journal of Fuzzy Systems
Year Published: 2018

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.