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An Experimental Analysis on the Sensitivity of the Most Widely Used Edge Detection Methods to Different Noise Types by Md Nishat Raihan; Noshin Ulfat; Nazmus Saqib is a scholarly article available to read on EtoBox.

What is An Experimental Analysis on the Sensitivity of the Most Widely Used Edge Detection Methods to Different Noise Types about?

Edge detection is a widely used low-level operation done in computer vision applications and image processing tasks. The primary purpose of edge detection is to discover and distinguish sharp discontinuities from an image. Since edges give boundaries between different regions in the image, these boundaries can be used to classify objects for segmentation and matching purposes. This is the first step in many computer vision applications. Edge detection remarkably reduces the amount of data and filters out undesired or less significant information and gives useful information in an image. There are some widely used algorithms for edge detection, some of which perform better than others but might require higher computational power. But all of them face several difficulties like false edge detection, issues due to noise, and missing of low contrast boundaries. Such difficulties are handled with other algorithms, if the data set has few images. But when the data set is enormous, removing noises and detecting edges simultaneously is very costly. So, a general idea regarding how noise resistant different edge detection algorithms are is necessary. In this paper, we have compared the perfo

Author

Md Nishat Raihan; Noshin Ulfat; Nazmus Saqib

Publisher

ACM

Published

2022

Language

EN