Information Sets for Edge Detection

Authored by: Arora Shaveta , Kothapalli Vignesh , Madasu Hanmandlu , Gupta Gaurav

Encyclopedia of Image Processing

Print publication date:  November  2018
Online publication date:  November  2018

Print ISBN: 9781482244908
eBook ISBN: 9781351032742
Adobe ISBN:

10.1201/9781351032742-140000272

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Abstract

In most of the computer vision applications, edge detection is the initial step for performing high-level tasks such as object recognition and scene analysis. The traditional algorithms cannot meet the desired accuracy and robustness of these applications. Information set theory is utilized for defining edge strength measures that help in finding the robust edges in the presence of noise. Information sets are derived from fuzzy sets by the application of Hanman–Anirban entropy. Fuzzy sets represent the vagueness present in an image using membership functions (MFs), whereas information sets represent the overall uncertainty in an image by linking the information source (any property/attribute) values with the corresponding MF values. Information set theory is already applied successfully to image processing applications such as enhancement of underexposed and overexposed images, face recognition, and gait recognition. The effectiveness of this theory is demonstrated here through the nonderivative smallest univalue segment assimilating nucleus (SUSAN) edge detector and Sobel fractional gradient by extracting localized features such as the fine edges in the image.

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