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
Run the EdgeDetection.ipynb file in Jupyter Notebook or Google Colab
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📦Edge-Detection
┣ 📂Results
┃ ┣ 📜output.png
┃ ┣ 📜output_1.png
┃ ┗ 📜output_3.png
┣ 📜.gitignore
┣ 📜Edge_Detection.ipynb
┣ 📜LICENSE
┗ 📜README.md