Canny Edge Detection is a popular edge detection algorithm. It was developed by John F. Canny It is a multi-stage algorithm and I will go through each stages.
Since edge detection is susceptible to noise in the image, first step is to remove the noise in the image with a 5x5 Gaussian filter. Smoothing using Well Known Gaussian filter Function It is inevitable that all images taken from a camera will contain some amount of noise. To prevent that noise is mistaken for edges, noise must be reduced. Therefore the image is first smoothed by applying a Gaussian filter.
Finding Intensity Gradient of the Image
Smoothened image is then filtered with a Sobel kernel in both horizontal and vertical direction to get first derivative in horizontal direction ( Gx) and vertical direction ( Gy). Finding gradients The Canny algorithm basically finds edges where the grayscale intensity of the image changes the most. These areas are found by determining gradients of the image. Gradients at each pixel in the smoothed image are determined by applying what is known as the Sobel-operator.
Non-maximum suppression Function. The purpose of this step is to convert the blurred edges in the image of the gradient magnitudes to sharp edges. Basically this is done by preserving all local maxima in the gradient image, and deleting everything else. The algorithm is for each pixel in the gradient image In short, the result you get is a binary image with "thin edges".
Edge tracking by hysteresis Strong edges are interpreted as “certain edges”, and can immediately be included in the final edge image. Weak edges are included if and only if they are connected to strong edges. The logic is of course that noise and other small variations are unlikely to result in a strong edge (with proper adjustment of the threshold levels). Thus strong edges will (almost) only be due to true edges in the original image. The weak edges can either be due to true edges or noise/color variations. The latter type will probably be distributed independently of edges on the entire image, and thus only a small amount will be located adjacent to strong edges. Weak edges due to true edges are much more likely to be connected directly to strong edges.
In short, This stage also removes small pixels noises on the assumption that edges are long lines. Read More On OpenCV
Example python command to run in terminal; python edge_detector.py im01.jpg
- Python:2.7, numpy, PIL, cv2 , m:tplotlib.pyplot
- IDE: PyCh:rm 2018.2.4 (Community Edition)
- All Canny Edge Detection's each stages are plotted on figure. The Stages are as following;
- Fatih Şennik
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