Interpret the steps of machine vision edge detection algorithm

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. So what are the steps of the machine vision edge detection algorithm? The following Chengdu Xinxiwang editor will give you the answer:

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

(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. A simple edge detection criterion is the gradient magnitude 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.