Briefly describe the steps of machine vision edge detection algorithm

Edge detection is a type of machine vision inspection technology . Its main applications include detecting whether chip pins are regular and neat, target positioning, and presence/defect detection. Based on the application of edge detection technology, it provides strong technical support for high-precision detection and dimensional measurement in the industry. Below, the editor of Xinxiwang will explain to you the steps of edge detection related algorithms:

1. Filtering:

Edge detection algorithms are mainly based on the first and second order derivatives of image intensity, but the calculation of the derivatives is sensitive to noise, so filters must be used to improve the performance of edge detectors related to noise. It should be noted that most filters also cause a loss of edge strength while reducing noise, so there is a trade-off between edge enhancement and noise reduction.

Machine vision edge detection algorithm

2. Enhancement:

The basis of edge enhancement is to determine the change value of the neighborhood intensity of each point in the image. The enhancement algorithm can highlight points with significant changes in neighborhood (or local) intensity values. Edge enhancement is generally accomplished by calculating gradient amplitude.

3. Detection:

There are many points in the image with relatively large gradient amplitudes, and these points are not all edges in specific application fields, so some method should be used to determine which points are edge points. *The simple edge detection criterion is the gradient amplitude threshold criterion.

4. Positioning:

If an application requires determining the edge position, the edge position can be estimated at sub-pixel resolution, and the edge orientation can also be estimated.

       It can be seen from this that edge detection is a type of machine vision detection technology. In the edge detection algorithm, the first three steps are very commonly used. This is because in most cases, the edge detector is only required to point out that the edge appears near a certain pixel in the image, but there is no need to point out the precise location or direction of the edge.

       The essence of edge detection is to use a certain algorithm to extract the boundary between the object and the background in the image. We define an edge as the boundary of a region in an image where the grayscale changes sharply. The changes in image grayscale can be reflected by the gradient of the image grayscale distribution, so we can use local image differentiation technology to obtain the edge detection operator. The classic edge detection method achieves the purpose of edge detection by constructing an edge detection operator for a small neighborhood of pixels in the original image.