There are many applications for machine vision light source detection, among which edge detection is also one of the important applications. Today, we will explain the steps of edge detection algorithms.

The steps of machine vision light source edge detection related algorithms are as follows:

1. Filtering: The edge detection algorithm is mainly based on the first and second derivative of image strength, but the calculation of derivatives is very sensitive to noise, so it is necessary to use filters to improve the performance of edge detectors related to noise. It is pointed out that most filters incur a loss of edge strength while reducing noise, so there is a need for a compromise between strengthening edges and reducing noise.

2. Strengthening: The foundation of strengthening edges is to confirm the changes in the strength of the neighborhood of each point in the image. Strengthening algorithms can highlight points with significant changes in neighborhood (or partial) strength values. Edge reinforcement is usually achieved by calculating the gradient amplitude.

3. Detection: There are many points in the image with large gradient amplitudes, and these points are not all edges in specific application categories. Therefore, some method should be used to determine which points are edge points. The simplest edge detection criterion is the gradient amplitude threshold criterion.

4. Positioning: If a certain application requests a positive edge position, the position of the edge can be estimated at sub pixel resolution, and the orientation of the edge can also be estimated.

Edge detection is a type of machine vision light source detection technology, and the first three steps are commonly used in edge detection algorithms. This is because in most places, it is only necessary for the edge detector to indicate that the edge appears to the left or near of a certain pixel in the image, without necessarily indicating the exact position or direction of the edge.

The essence of edge detection is to use a certain algorithm to extract the boundary line between the object and the background in the image. We define edges as the boundaries of areas in the image where the grayscale changes sharply. The changes in image grayscale can be reflected by the gradient of image grayscale dispersion, so we can use partial image differentiation techniques to obtain edge detection operators. The classic edge detection method achieves the goal of detecting edges by performing edge detection operators on a small neighborhood structure of pixels in the original image.

The main applications of edge detection include: detecting whether chip pins can be regularly aligned, target positioning, and presence/defect detection. The application of edge detection technology provides strong technical support for high-precision detection and size measurement in the industry.