Canny Edge Detection

Edges are one of the most important features in an image. Edges are basically areas with high intensity contrast. Canny edge detection algorithm developed by John F. Canny in 1986 is a multi-stage optimal edge detector. It is carried out as follows:

Noise Reduction
Since every image is susceptible to noise a Gaussian filter is applied and the image is smoothed.

Intensity Gradient
A Sobel filter is applied to smoothed image in both x and the y axis. This gives the edge gradients and direction of the edge pixels.

Non-maximum Suppression
Every pixel extracted out by the Sobel filter is tested for whether it constitutes an edge or not.
This is done by testing if the pixel is at a local maximum in its neighbourhood in the direction of gradient.
This step basically thins out the edges.

Hysteresis Thresholding
This final tests if all the detected edges are real or dummy. Two threshold values minVal and maxVal are set. Any edge with intensity gradient above maxVal is an edge an those below minVal are discarded. The pixels which lie in between if are discarded if they are not connected to a sure edge.

A simple example to show how Canny edge detection is carried out using python:

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('circle.png',0)
edges = cv2.Canny(img,100,200)

plt.subplot(121),plt.imshow(img,cmap = 'gray')
plt.title('Original Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(edges,cmap = 'gray')
plt.title('Edge Image'), plt.xticks([]), plt.yticks([])

The result:

An application for Canny Edge detection with trackbars to adjust the Hysteresis threshold values:


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