About Edge Detection in Digital Images

Abstract

Edge detection is one of the most commonly used procedures in digital image processing. In the last 30-40 years, many methods and algorithms for edge detection have been proposed. This article presents an overview of edge detection methods, the methods are divided according to the applied basic principles. Next, the measures and image database used for edge detectors performance quantification are described. Ordinary users as well as authors proposing new edge detectors often use Matlab function without understanding it in details. Therefore, one section is devoted to some of Matlab function parameters that affect the final result. Finally, the latest trends in edge detection are listed. Picture Lena and two images from Berkeley segmentation data set (BSDS500) are used for edge detection methods comparison.

Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science

“…This method assumes that discontinuation exists between the foreground and background, wherein the values of pixels connecting them are distinct. These discontinuities are typically detected by the first and the second order derivatives such as gradient and Laplace . The gradient operator, like Sobel and Prewitt, use first order derivatives, and are easy to implement.…”

“…These discontinuities are typically detected by the first and the second order derivatives such as gradient and Laplace. 28 The gradient operator, like Sobel and Prewitt, use first order derivatives, and are easy to implement. They are roughly able to estimate the contour profile, but are highly sensitive to the noise.…”

Hyperanalytic Wavelet-Based Robust Edge Detection

“…The most used edge detection operators are Prewitt, Sobel, Robinson, and Kirsch [1]. Then the edge strength ES(x,y) can be estimated as the gradient vector's magnitude and so we can detect the edges as the set of pixels having maximum gradients.…”

Analysis of Edge Detection on Compressed Images with Different Complexities

“…We are also witnessing an increase in the use of smart networks, the use of artificial intelligence to analyze, collect and process data. Such systems are mainly based on image processing and data processing, where the main processes are the extraction of a particular object from the scene, where edge detection and segmentation play an important role [31][32][33][34]. However, all of this gain particular weight and interest with the emergence and implementation of such systems on devices like Raspberry Pi and Arduino, which very often use real-time image processing, object detection and segmentation [35][36][37][38][39].…”