The visual recognition system of CCD screen printing machine usually uses multiple image algorithms to achieve accurate recognition and positioning of screen printing patterns. The following are some common algorithms:

Template matching algorithm

This is a basic and commonly used algorithm. It determines the position and orientation of the pattern by comparing pre stored standard pattern templates with real-time collected silk screen images, calculating the similarity between the two. For example, when screen printing a logo on a mobile phone screen, the system will compare the pre entered logo template with the actual image of the screen printing area point by point. Common similarity calculation methods include normalized cross-correlation algorithm, etc. The area with the highest similarity is considered to be the position of the target pattern.

Edge detection algorithm

This algorithm is mainly used to extract edge information of objects in images. The edges of silk screen patterns often have significant grayscale changes, and edge detection algorithms such as Canny algorithm, Sobel algorithm, etc. can accurately detect the edge contour of the pattern. Taking the circuit pattern on a printed circuit board as an example, edge detection algorithms can clearly outline the edges of the circuit lines, thereby determining the shape and position of the pattern, providing accurate positioning basis for subsequent silk screen printing operations.

Feature extraction and matching algorithm

This algorithm extracts feature points and feature descriptors from the image, and then identifies patterns by matching features. For example, SIFT (Scale Invariant Feature Transform) algorithm and SURF (Accelerated Robust Feature) algorithm can extract stable features of images at different scales and rotation angles. When the screen printing machine screens products of different angles and sizes, these algorithms can accurately find the corresponding feature points and match them, thus achieving precise visual recognition.

Threshold segmentation algorithm

It is an algorithm that performs segmentation based on the grayscale value of the image. By setting one or more thresholds, pixels in an image are classified into different categories, typically foreground and background. In the visual recognition of CCD screen printing machines, threshold segmentation algorithms can be used to separate screen printing patterns with significant grayscale differences from the background, facilitating subsequent analysis and processing. For example, when black text is printed on a white background through screen printing, appropriate threshold settings can quickly and accurately extract the text pattern for recognition.