As the demand for automation and intelligence in various industries across our country continues to rise, machine vision technology has permeated into our daily lives and work environments, widely applied in sectors such as semiconductor, electronics, industry, scientific research, and transportation. For instance, quality inspection and monitoring in industrial production and color quality testing in the printing industry. The increase in market demand not only promotes the wider application of products but also stimulates the continuous improvement of product quality. The advancement of machine vision technology is reflected in the enhancement of camera quality and functionality, the richness of light source forms, and the upgrade from grayscale to color image capture.

Generally, we know that color is formed by the combination of the three primary colors—red, green, and blue. In machine vision applications, however, color also involves considerations of saturation, chroma, and hue. Therefore, color image processing and analysis software must be able to distinguish the colorless component, or saturation, from the chroma and hue descriptions to intuitively and completely explain color and effectively detect and segment color objects. In machine vision applications, we often encounter color imbalance issues, primarily caused by two factors: the type of light used and the incorrect calibration of the image acquisition device.

The essence of image analysis in machine vision technology is the detection and segmentation of color objects. The detection of color objects is easy to understand, but what does segmentation refer to? Segmentation refers to the ability of machine vision technology to distinguish objects from the background of an image based on contrast. In the case of color images, you can freely specify the numerical range of a particular object, then select the data range to eliminate surface light effects. You can also analyze the color images at each pixel level for different objects in the scene, thereby decomposing the color images in different regions.

The collection and analysis of color images are in high demand across various industries and represent a direction for future development. Compared to grayscale collection, color image capture yields clearer images, making them easier to analyze and process, and thus more valuable for production and scientific research. Of course, as mentioned earlier, continuous advancement in machine vision technology is necessary to achieve desired results and meet collection needs.