Non-paper material image capture and surface feature detection technology
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With the development of printing technology and the advancement of materials technology, printing has been widely used in various industries. Printing materials have gradually expanded from traditional papers to plastics, leather, ceramic plates, glass and other multi-materials. In the era when the trip can only be viewed under the standard light box, the consideration of "environmental light source" has become a new element of future color reproduction. Therefore, a unique and efficient method must be established to control color. How to integrate the “environmental light source” into the color management process and copy it with the virtual look of the display and the precise printing process is an important topic in the development of graphic color reproduction technology.
The Print Research Center collaborated internationally with the University of Leeds in the United Kingdom in 101. The research team was led by Professor Luo Ming to study non-paper material image capture and surface feature detection technology. For non-paper prints, high color accuracy displays were used. The development of the overall visual appearance simulation system under multi-source illumination includes high-order digital cameras, multi-spectral measurement systems, precision bearing platforms, image processing technologies, and so on. Complete the visual characteristics of non-paper materials (stone, textile) surface color, texture, gloss, etc., and complete the database establishment. Professor Luo Ming and the research team have cooperated with the Printing Research Center for many times to develop innovative technologies related to image and printing, and to assist domestic manufacturers in the printing industry through the Printing Research Center.
The color and imaging research unit of the University of Leeds in the UK specializes in the application of color science and imaging theory, plus measurement, using cutting-edge computer software and hardware technology, working on color and image, and participating in ISO color standard research work. At the same time, on the other hand, he participated in the work of the CIE Lighting and Optics Committee. Therefore, the research on color has included excellent results from reflection to self-illumination. In recent years, the unit has extended its footsteps to non-color theory, but psychological and environmental factors. One person, a group of people, and people of different cultural backgrounds will have different color perceptions and gaps, so color perception is mathematical. The mode definition, what is the difference value that can occur.
Non-paper materials image capture and surface feature detection techniques are used as a comparison of different image capture methods for the appearance characteristics of objects. The development of a calculation system currently shows the simulation results of the appearance characteristics of two sets of materials (textiles and stone) under different observation conditions. The technical highlights are outlined below.
First, the two-way reflection distribution measurement
Non-paper materials (for example, textile samples and stone samples) After the light source is illuminated by different angles, the surface reflectance function is different. The CCD camera lens is used to receive the surface reflection and scattered light of the object, and the surface gloss of the object is measured. In the stone sample, it is divided into three samples with different glossiness. The measured data is analyzed to obtain the bidirectional reflection distribution function of the surface, which represents the surface gloss observation of the material. The bidirectional reflectance distribution function data is applied to two different reflectance estimation modules (Phong and Torrance-Sparrow) to estimate the gloss of the mirror and matte stone samples.
Second, multi-angle image capture
The Gonio-image Capture System workflow begins by capturing the image of the object, defining the surface texture of the object under different light conditions, and then recombining the image pixels in the same chromaticity plane, and finally measuring the results of the system. Import as a reference for the simulated image texture.
Third, the sample appearance simulation display software
The camera captures the image color description for the correct conversion of the digital camera RGB color and gamut space CIE XYZ. The high-order polynomial regression method of the digital camera RGB color is used to establish a conversion module of the rule of thumb. The appearance simulation display software simulates the visual effects of the gloss and texture of the sample under different light source conditions and is compared with the visual evaluation results. The flow chart of the simulation software can be divided into 1. Initialization. 2. Color. 3. Texture. 4. Light source. 5. The results are displayed.
This demonstrates the software performing a psychophysical experiment with seven discriminant scales to evaluate the difference between the simulated image and the camera's reference image. The assessment results are summarized into two points:
1. The performance of the Torrance-Sparrow Model with different angles of incidence is better than that of the Phone Model. When the angle of incidence of light increases, the performance of the two models becomes worse.
2. Performance comparison of different materials. In most materials, the Torrance-Sparrow Model still outperforms PhoneModel. The textile materials of these two models have better performance than stone.
This collaboration is to use the R&D energy of the University of Leeds and the Print Research Center to provide an effective method for multi-spectral image color information capture, and to combine the results of psychophysical analysis to define different color perceptions, expecting to be accurate and The accepted appearance is copied. In the future, the Printing Research Center will continue to collaborate with the University of Leeds to develop analog correction techniques for cross-media image color or material appearance. Applied to domestic technology breakthroughs in the printing industry, it will assist in the upgrading of industrial technology and expand the field of image capture and printing and copying services.