How do you choose the optimal parameters for Canny edge detection?

1 Noise reduction

Before applying Canny edge detection, you need to reduce the noise in the image. Noise can affect the accuracy and smoothness of the edges. One way to do that is to use a Gaussian blur filter, which smooths out the pixels by averaging them with their neighbors. The size and standard deviation of the filter determine how much noise is removed and how much detail is preserved. A larger filter will remove more noise but also blur the edges, while a smaller filter will keep more detail but also keep more noise. You can experiment with different values to find the optimal balance for your image.

2 Gradient computation

The next step is to compute the gradient of the image, which measures the change in intensity along the x and y directions. The gradient indicates the direction and magnitude of the edge at each pixel. You can use different operators to calculate the gradient, such as Sobel, Prewitt, or Roberts. The choice of operator affects the sensitivity and robustness of the edge detection. A more sensitive operator will detect more edges but also more noise, while a more robust operator will ignore more noise but also more weak edges. You can compare the results of different operators to see which one suits your image.

3 Non-maximum suppression

The gradient image may contain thick or multiple edges that are not desirable. To thin out the edges, you can use non-maximum suppression, which checks each pixel and keeps only the ones that have the maximum gradient magnitude along the gradient direction. This way, only the strongest edge pixels remain and the others are set to zero. Non-maximum suppression does not require any parameters, but it depends on the accuracy of the gradient computation.

4 Hysteresis thresholding

The final step is to apply hysteresis thresholding, which separates the edge pixels into strong and weak ones based on two threshold values: low and high. The strong edge pixels are the ones that have a gradient magnitude above the high threshold, while the weak edge pixels are the ones that have a gradient magnitude between the low and high thresholds. The strong edge pixels are kept as part of the final edge map, while the weak edge pixels are either kept or discarded depending on whether they are connected to a strong edge pixel or not. This process helps to eliminate spurious edges and preserve continuous edges. The choice of the low and high thresholds affects the number and quality of the edges. A lower low threshold will include more weak edges, while a higher high threshold will exclude more strong edges. You can use a trial-and-error method or a histogram analysis to find the optimal threshold values for your image.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?