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Edge detection includes a variety of mathematical methods that aim at identifying edges, curves in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. This program explores various methods to approach edge detection

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Edge-Detection

Edge detection includes a variety of mathematical methods that aim at identifying edges, curves in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. This program explores various methods to approach edge detection

Overview

The main steps of edge detection are: (1) assign a score to each pixel; (2) find local maxima along the direction perpendicular to the edge. Sometimes a third step is performed where local evidence is propagated so that long contours are more confident or strong edges boost the confidence of nearby weak edges. Optionally, a thresholding step can then convert from soft boundaries to hard binary boundaries. Here are sample outputs.

Hint: Reading these papers will help understanding and may help with the programming.

The design and use of steerable filters

Berkeley Pb Detector

Multi-scale edge detection

Usage

Run the EdgeDetection.ipynb file in Jupyter Notebook or Google Colab

Results

Folder Structure

📦Edge-Detection
 ┣ 📂Results
 ┃ ┣ 📜output.png
 ┃ ┣ 📜output_1.png
 ┃ ┗ 📜output_3.png
 ┣ 📜.gitignore
 ┣ 📜Edge_Detection.ipynb
 ┣ 📜LICENSE
 ┗ 📜README.md

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Edge detection includes a variety of mathematical methods that aim at identifying edges, curves in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. This program explores various methods to approach edge detection

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