Edge detection-detection lens

The edge information of an image is very important for people or for machine vision. Because the edge has many advantages such as the shape of the area, and can be locally defined, and it can transfer most of the image information, edge detection can be regarded as the key to deal with many complex problems, and it is the first of image analysis and understanding. In step, the image with edges detected can be used for feature extraction and shape analysis. Because the edges are the result of discontinuity in gray values, such discontinuities can often be easily detected using derivatives, and first and second derivatives are generally selected to detect edges. In machine vision inspection, spatial domain differential operators are often used

  In fact, the edge information of an image is very important for humans or for machine vision. Because the edge has many advantages such as the shape of the area, and can be locally defined, and it can transfer most of the image information, edge detection can be regarded as the key to deal with many complex problems, and it is the first of image analysis and understanding. In step, the image with edges detected can be used for feature extraction and shape analysis.

  Because the edges are the result of discontinuity in gray values, such discontinuities can often be easily detected using derivatives, and first and second derivatives are generally selected to detect edges. In machine vision detection, convolution is often used to achieve this by means of a spatial domain differential operator (actually a differential approximation of the differential operator). Differential operators commonly used are gradient operators and Laplace operators.

Edge detection can be done by convolution with the help of spatial differential operators. In fact, in digital image processing, the derivative is obtained by using differential approximate differentiation. Differential operators commonly used are gradient operators and Laplace operators.

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 derivatives of the image intensity, but the calculation of the derivatives is very sensitive to noise, so a filter must be used to improve the performance of the noise-related edge detector.

2. Enhancement: The basis for enhancing the edge is to determine the change in the intensity of the neighborhood of each point in the image. The enhancement algorithm can highlight points with significant changes in neighborhood (or local) intensity values.

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

Inspection lens

4. Positioning: If the edge position needs to be determined in an application, the position of the edge can be estimated at the sub-pixel resolution, and the position of the edge can also be estimated.

These four steps are indispensable when using machine vision for dimensional measurements, and in particular the precise location and orientation of the edges must be indicated. Machine vision inspection technology, with its powerful performance advantages, has standardized product quality, fast inspection speed, reliable and stable inspection results, and can be inspected for a long time. It is widely used in various fields-inspection lenses