Comprehensive Guide to Canny Edge Detection with Examples
Edge detection is a critical operation in image processing, it is used to identify boundaries between the objects or regions in an image. There are several methods for edge detection, but one of the most widely used is the Canny Edge Detection method.
What is Canny Edge Detection?
Canny Edge Detection is a multi-stage edge detection algorithm that was developed by John Canny in 1986. It is widely used due to its ability to preserve edges while suppressing noise. The Canny Edge Detection method uses gradient information to detect edges, and it is considered to be one of the best edge detection methods due to its accuracy and efficiency.
The Canny Edge Detection method consists of the following stages:1
1 – Gaussian Blur: The input image is smoothed using a Gaussian filter to remove noise.
2 – Gradient Calculation: The gradient of the image intensity is calculated using the Sobel operator or another gradient operator.
3 – Non-maximum Suppression: The gradient magnitude is thresholded to remove weak edges and keep only the strongest edges.
4 – Hysteresis Thresholding: The remaining edges are processed using hysteresis thresholding, where edges are traced from strong edges to weak edges.
The result of the Canny Edge Detection method is a binary image where the edges are represented by white pixels, and the background is represented by black pixels.
Example of Canny Edge Detection in Python and OpenCV:
Output images:
In this example, the input image is first smoothed using a Gaussian filter to remove noise. Then, we applied Canny Edge Detection method to the smoothed image, and the result is displayed using the imshow function from the matplotlib library.
The Canny Edge Detection method takes three parameters: img, the input image; minVal, the lower threshold for edges; and maxVal, the upper threshold for edges. In this example, minVal is set to 100 and maxVal is set to 200, but these values can be adjusted based on the specific requirements of the task at hand.
Conclusion
Canny Edge Detection is a powerful and widely used edge detection method that is capable of preserving edges while suppressing noise. It consists of several stages, including Gaussian Blur, gradient calculation, non-maximum suppression, and hysteresis thresholding, that work together to produce accurate and efficient edge detection results. The Canny Edge Detection method is an important tool in image processing, and it can be used in a wide range of applications, including object recognition, image segmentation, and pattern recognition.