Canny Edge Detection is a popular algorithm for identifying edges in images. It combines multiple steps to detect sharp changes in intensity, making it ideal for tasks like object recognition and image segmentation. Below is a step-by-step guide:

1. Gaussian Filtering

  • Apply a Gaussian filter to reduce noise and smooth the image.
Gaussian_Filtering
- This step ensures that the gradient calculation is accurate by eliminating irrelevant details.

2. Gradient Magnitude & Direction

  • Use the Sobel operator to compute the gradient magnitude and direction.
Sobel_Operator
- The gradient helps identify regions of rapid intensity change.

3. Non-Maximum Suppression

  • Thin the edges by suppressing non-maximum values in the gradient direction.
Non_Maximum_Suppression
- This step sharpens the edges while preserving their structure.

4. Double Thresholding

  • Apply two thresholds to identify strong and weak edges.
Double_Thresholding
- Strong edges are kept, while weak edges are discarded unless they are part of a continuous line.

5. Edge Tracking by Hysteresis

  • Trace edges by connecting weak edges to strong ones.
Hysteresis_Tracking
- This final step ensures only significant edges are retained.

For a deeper dive into OpenCV implementations, check out our OpenCV Tutorials Page. 🚀