An implementation of two famous edge detectors

1. Canny edge detector

Description

The Canny edge detector uses a multi-stage algorithm to detect edges in images. It was developed by John F. Canny in 1986. Canny also produced a computational theory of edge detection explaining how the technique works.

Steps Involved

Apply Gaussian filter to smooth out the image
Find intensity gradients from the given image
Apply non-maximum suppression to remove spurious response to edge detection
Apply double threshold to determine potential edges
Track edge by hysteresis: finalize the detection of edges by suppressing all the other edges that are weak and not connected to strong edges (not done)

Motivation

John Canny's paper on "A Computational Approach to Edge Detection".

Result

How to run

This program depends on the following packages:

Matplotlib
Skimage
NumPy
mpl_toolkits
argparse

Clone this repository with:

git clone https://github.com/adl1995/edge-detectors.git

Execute the script with:

python canny-edge.py --input <input image path> --output <output image path> --sigma <optional> --th1 <optional> --th2 <optional>

2. Marr Hildreth

Description

The Marr-Hildreth algorithm finds edges in digital images where there are strong and rapid variations in the image brightness. The Marr–Hildreth edge detection method operates by convolving the image with the Laplacian of the Gaussian function, or, as a fast approximation by Difference of Gaussians (DoG). Then, zero crossings are detected in the filtered result to obtain the edges.

Steps Involved

Apply Gaussian filter to smooth out the image
Find zero crossings

Motivation

Hildreth's paper on "Theory of edge detection".

Result

How to run

This program requires Python 3.6+ and depends on the following packages:

Matplotlib
Skimage
NumPy
argparse
mpl_toolkits

Execute the script with:

python marr-hildreth-edge.py --input <input image path> --output <output image path> --sigma <optional>

adl1995/edge-detectors

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