Canny Edge Detection Algorithm: A Comprehensive Guide

Canny algorithm is a classic edge detection algorithm widely used in image processing. The basic idea behind Canny algorithm is: first, smoothing the image using a Gaussian filter; then, calculating the gradient intensity and direction for each pixel in the image; followed by extracting edges through non-maximum suppression and double threshold detection. Here are the specific steps of the Canny algorithm:

1. Gaussian filtering: Use a Gaussian filter to smooth the original image and remove noise.

2. Gradient calculation: Perform derivative operations on the smoothed image to obtain the gradient intensity and direction of each pixel.

3. Non-maximum suppression: Perform non-maximum suppression in the gradient direction, keeping the non-maximum points in the gradient direction and suppressing other points.

4. Double threshold detection: Set two thresholds, mark points with gradient intensity greater than the high threshold as edge points, exclude points with gradient intensity less than the low threshold, and mark points with gradient intensity between the two thresholds as weak edge points.

5. Edge connection: Connect weak edge points to high threshold edge points to generate complete edges.

Advantages of Canny algorithm include its effectiveness in removing noise from images and extracting clear and continuous edges. However, it has drawbacks such as high computational complexity, requiring multiple filtering and processing steps, making the algorithm relatively complex.