The video images of the printed products are continuously taken by a camera set on the production line, and the speed of the recording is adjustable at 30 frames/s or less. The image captured by the camera is first quantified, the analog signal is converted into a digital signal, and a key frame that effectively represents the content of the lens is extracted from it and displayed on the display. For a frame of image, an analysis method for a still image may be used to process. Through size measurement and multi-spectral analysis, color patches on a video image may be identified, and color space and color parameters of the color patch may be obtained as well as some other correlations.
Due to various factors, various types of noise may occur, such as Gaussian noise, salt and pepper noise, and random noise. Noise brings many difficulties to image processing. It has a direct impact on image segmentation, feature extraction, and image recognition. Therefore, real-time acquired images need to be filtered. Image filtering requires the removal of noise outside the image while maintaining the details of the image. When the noise is Gaussian noise, the most commonly used linear filter is easy to analyze and implement; however, the linear filter has poor filtering effect on salt and pepper noise, and the traditional median filter can reduce the salt and pepper noise in the image, but the effect Not ideal, that is, fully dispersed noise is removed, and the noise close to each other will be retained, so when the salt and pepper noise is more serious, its filtering effect is significantly worse. This system improved median filtering method. The method firstly obtains the median value after removing the maximum and minimum grayscale pixels in the noise image window, and then calculates the difference between the median value and the corresponding pixel grayscale value, and then compares it with the threshold to determine whether to use the obtained value. Instead of the grayscale value of the pixel.
Image segmentation detects color patches and separates them from the background at this stage. The edge of the object is represented by gray-scale discontinuity. The edge type can be divided into two types. The first is the step edge, and the pixels on both sides are The gray value is significantly different; the second is the roof-like edge, which lies in the change of the gray value from increasing to decreasing turning point L. For the step edge, the second-order directional derivative is zero-crossing at the edge, so the differential calculation is available. Son to do edge detection operator. The differential operator class edge detection method is similar to the high-pass filter in the high spatial domain and has the effect of increasing high-frequency components. Such operators are quite sensitive to noise. For step edges, the commonly available operators include the gradient operator Sobel operator. Child and Kirsh operators. Laplace transforms and Kirsh operators are available for roof-like edges. Because the color mark is rectangular and the gray levels of adjacent edges are quite different, edge detection is used to divide the image. Here we use Sobert edge to perform edge detection. It uses the local difference operator to find the edge, which can separate the color marks better. In the actual detection process, a color image edge detection method is adopted, and an appropriate color base (such as intensity, chroma, saturation, etc.) is selected for detection. According to the type characteristics of the printing press, that is, the color of the printing machine and the characteristics of the layout, multi-threshold processing is performed to obtain binary maps of various colors.
The segmented image is measured and the object is identified by the measured value. Since the color patch is a rectangle with a regular shape, the following features can be extracted: (1) Calculate the area of ​​a rectangle by pixels, (2) Rectangularity, (3) ) Chromaticity (H) and Saturation (S), and then the spacing between the color patches is obtained according to the number of pixels in the interval between each color standard. Compared with the set value, the difference between the two is obtained, and a total of m measurements are performed to obtain the average value. The difference provides a corresponding adjustment signal to the digital AC servo adjustment section. To adjust the relative position of the color roller to eliminate or reduce misalignment. In feature extraction, multispectral image analysis is performed on the image to quantitatively represent color patches, such as the color of pixels in a color number image. The HIS format is used to obtain two parameters of the color information of each color standard: chroma and saturation. To check the quality of the ink. The statistical analysis of the binary image of each color and the matching of the template with the standard graphics are performed to measure the parameters such as the ink scraps during the printing process.
The printing machine is unwound from the uncoiler and passes through the printing units in sequence to carry out printing and drying of various colors. The winding machine performs the winding and each color printing prints a color mark on the edge of the printing material for color registration. The color markings are 10 mm horizontal and 1 mm wide. The marking lines of each adjacent color should be parallel to each other when they are accurately printed, and the vertical (vertical) distance is 20 mm. Video images of printed products are continuously taken by the cameras installed on the production line. Through the size measurement and multi-spectral analysis can identify the color image on the video, get the color space and the color parameters of the color L If the interval between adjacent two color color is greater than or less than 20 mm, it indicates overprinting deviation. The deviation signal is sent to the servo variable frequency drive unit to drive the AC servo motor to move the corresponding chroma correction roller ML up and down to extend or shorten the stroke of the printing material from the previous unit printing plate roller to the printing plate roller to dynamically correct.
Due to various factors, various types of noise may occur, such as Gaussian noise, salt and pepper noise, and random noise. Noise brings many difficulties to image processing. It has a direct impact on image segmentation, feature extraction, and image recognition. Therefore, real-time acquired images need to be filtered. Image filtering requires the removal of noise outside the image while maintaining the details of the image. When the noise is Gaussian noise, the most commonly used linear filter is easy to analyze and implement; however, the linear filter has poor filtering effect on salt and pepper noise, and the traditional median filter can reduce the salt and pepper noise in the image, but the effect Not ideal, that is, fully dispersed noise is removed, and the noise close to each other will be retained, so when the salt and pepper noise is more serious, its filtering effect is significantly worse. This system improved median filtering method. The method firstly obtains the median value after removing the maximum and minimum grayscale pixels in the noise image window, and then calculates the difference between the median value and the corresponding pixel grayscale value, and then compares it with the threshold to determine whether to use the obtained value. Instead of the grayscale value of the pixel.
Image segmentation detects color patches and separates them from the background at this stage. The edge of the object is represented by gray-scale discontinuity. The edge type can be divided into two types. The first is the step edge, and the pixels on both sides are The gray value is significantly different; the second is the roof-like edge, which lies in the change of the gray value from increasing to decreasing turning point L. For the step edge, the second-order directional derivative is zero-crossing at the edge, so the differential calculation is available. Son to do edge detection operator. The differential operator class edge detection method is similar to the high-pass filter in the high spatial domain and has the effect of increasing high-frequency components. Such operators are quite sensitive to noise. For step edges, the commonly available operators include the gradient operator Sobel operator. Child and Kirsh operators. Laplace transforms and Kirsh operators are available for roof-like edges. Because the color mark is rectangular and the gray levels of adjacent edges are quite different, edge detection is used to divide the image. Here we use Sobert edge to perform edge detection. It uses the local difference operator to find the edge, which can separate the color marks better. In the actual detection process, a color image edge detection method is adopted, and an appropriate color base (such as intensity, chroma, saturation, etc.) is selected for detection. According to the type characteristics of the printing press, that is, the color of the printing machine and the characteristics of the layout, multi-threshold processing is performed to obtain binary maps of various colors.
The segmented image is measured and the object is identified by the measured value. Since the color patch is a rectangle with a regular shape, the following features can be extracted: (1) Calculate the area of ​​a rectangle by pixels, (2) Rectangularity, (3) ) Chromaticity (H) and Saturation (S), and then the spacing between the color patches is obtained according to the number of pixels in the interval between each color standard. Compared with the set value, the difference between the two is obtained, and a total of m measurements are performed to obtain the average value. The difference provides a corresponding adjustment signal to the digital AC servo adjustment section. To adjust the relative position of the color roller to eliminate or reduce misalignment. In feature extraction, multispectral image analysis is performed on the image to quantitatively represent color patches, such as the color of pixels in a color number image. The HIS format is used to obtain two parameters of the color information of each color standard: chroma and saturation. To check the quality of the ink. The statistical analysis of the binary image of each color and the matching of the template with the standard graphics are performed to measure the parameters such as the ink scraps during the printing process.
The printing machine is unwound from the uncoiler and passes through the printing units in sequence to carry out printing and drying of various colors. The winding machine performs the winding and each color printing prints a color mark on the edge of the printing material for color registration. The color markings are 10 mm horizontal and 1 mm wide. The marking lines of each adjacent color should be parallel to each other when they are accurately printed, and the vertical (vertical) distance is 20 mm. Video images of printed products are continuously taken by the cameras installed on the production line. Through the size measurement and multi-spectral analysis can identify the color image on the video, get the color space and the color parameters of the color L If the interval between adjacent two color color is greater than or less than 20 mm, it indicates overprinting deviation. The deviation signal is sent to the servo variable frequency drive unit to drive the AC servo motor to move the corresponding chroma correction roller ML up and down to extend or shorten the stroke of the printing material from the previous unit printing plate roller to the printing plate roller to dynamically correct.
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