Tag Archives: sobel filter

Canny Edge Detector

In this blog, we will discuss one of the most popular algorithms for edge detection known as Canny Edge detection. It was developed by John F. Canny in 1986. It is a multi-stage algorithm that provides good and reliable detection. So, let’s discuss the main steps used in the Canny Edge detection algorithm using OpenCV-Python.

1. Noise Reduction

An edge detector is a high pass filter that enhances the high-frequency component and suppresses the low ones. Since both edges and noise are high-frequency components, the edge detectors tend to amplify the noise. To prevent this, we smooth the image with a low-pass filter. Canny uses a Gaussian filter for this.

Below is the code for this using OpenCV-Python

A larger filter reduces noise but worsens edge localization and vice-versa. Generally, 5×5 is a good choice but this may vary from image to image.

2. Finding Intensity Gradient of the Image

Next step is to find the edges using a Sobel operator. Sobel finds the gradients in both horizontal(Gx) and vertical(Gy) direction. Since edges are perpendicular to the gradient direction, using these gradients we can find the edge gradient and direction for each pixel as:

Below is the code for this using OpenCV-Python (Here, I’ve converted everything to 8-bit, it’s optional you can use any output datatype)

Clearly, we can see that the edges are still quite blurred or thick. Remember that an edge detector should output only one accurate response corresponding to the edge. Thus we need to thin the edges or in other words find the largest edge. This is done using Non-max Suppression.

3. Non-Max Suppression

This is an edge thinning technique. In this, for each pixel, we check if it is a local maximum in its neighborhood in the direction of gradient or not. If it is a local maximum it is retained as an edge pixel, otherwise suppressed.

For each pixel, the neighboring pixels are located in horizontal, vertical, and diagonal directions (0°, 45°, 90°, and 135°). Thus we need to round off the gradient direction at every pixel to one of these directions as shown below.

After rounding, we will compare every pixel value against the two neighboring pixels in the gradient direction. If that pixel is a local maximum, it is retained as an edge pixel otherwise suppressed. This way only the largest responses will be left.

Let’s see an example

Suppose for a pixel ‘A’, the gradient direction comes out to be 17 degrees. Since 17 is nearer to 0, we will round it to 0 degrees. Then we select neighboring pixels in the rounded gradient direction (See B and C in below figure). If the intensity value of A is greater than that of B and C, it is retained as an edge pixel otherwise suppressed.

Let’s see how to do this using OpenCV-Python

Clearly, we can see that the edges are thinned but some edges are more bright than others. The brighter ones can be considered as strong edges but the lighter ones can actually be edges or they can be because of noise.

4. Hysteresis Thresholding

Non-max suppression outputs a more accurate representation of real edges in an image. But you can see that some edges are more bright than others. The brighter ones can be considered as strong edges but the lighter ones can actually be edges or they can be because of noise. To solve the problem of “which edges are really edges and which are not” Canny uses the Hysteresis thresholding. In this, we set two thresholds ‘High’ and ‘Low’.

  • Any edges with intensity greater than ‘High’ are the sure edges.
  • Any edges with intensity less than ‘Low’ are sure to be non-edges.
  • The edges between ‘High’ and ‘Low’ thresholds are classified as edges only if they are connected to a sure edge otherwise discarded.

Let’s take an example to understand

Here, A and B are sure-edges as they are above ‘High’ threshold. Similarly, D is a sure non-edge. Both ‘E’ and ‘C’ are weak edges but since ‘C’ is connected to ‘B’ which is a sure edge, ‘C’ is also considered as a strong edge. Using the same logic ‘E’ is discarded. This way we will get only the strong edges in the image.

This is based on the assumption that the edges are long lines.

Below is the code using OpenCV-Python.

First set the thresholds and classify edges into strong, weak or non-edges.

For weak edges, if it is connected to a sure edge it will be considered as an edge otherwise suppressed.

OpenCV-Python

OpenCV provides a builtin function for performing Canny Edge detection

Let’s take an example

Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.