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

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

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

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