Edge detection based on machine vision technology

The edge information of an image is very important for both human and machine vision. Since the edge has many advantages such as being able to outline the shape of an area, being locally defined, and being able to transmit most of the image information, edge detection can be seen as the key to dealing with many complex problems and is the first step in image analysis and understanding. Images with detected edges can be used for feature extraction and shape analysis.

Since the edge is the result of discontinuity in grayscale value, this discontinuity can often be easily detected by taking derivatives. Generally, first-order and second-order derivatives are selected to detect edges. In machine vision detection, convolution is often used with the help of spatial differential operators (actually the differential approximation of differential operators). Commonly used differential operators include gradient operators and Laplace operators.

Edge detection can be done by convolution with the help of spatial differential operators. In fact, the derivative in digital image processing is done by using differential approximation. Commonly used differential operators include gradient operator and Laplace operator.

The basic steps of the edge detection algorithm are as follows:

1. Filtering: The edge detection algorithm is mainly based on the first and second order derivatives of the image intensity, but the calculation of the derivative is very sensitive to noise, so filters must be used to improve the performance of the edge detector related to noise.

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 the points with significant changes in neighborhood (or local) intensity values.

3. Detection: There are many points in the image with large gradient amplitudes, but these points are not all edges in specific application fields, so some method should be used to determine which points are edge points. The gradient amplitude Ill value criterion is often used.

4. Positioning: If an application requires the determination of the edge position, the edge position can be estimated at sub-pixel resolution, and the orientation of the edge can also be estimated.

When using machine vision for dimensional measurement, these four steps are essential, especially the precise position and orientation of the edge must be pointed out. Machine vision inspection technology, with its powerful performance advantages, standardizes product quality, has fast inspection speed, reliable and stable inspection results, and can inspect for a long time. It is widely used in various fields.