scholarly journals Intelligent Color Reproduction and Color Management System

1997 ◽  
Vol 81 (1) ◽  
pp. 44-46
Author(s):  
Katsunori Okajima
2020 ◽  
pp. 1-12
Author(s):  
Li Bo

In today’s society, graphic design, as a popular image processing technology, plays an increasingly important role in people’s lives. In the specific operation process of graphic design, It is no longer restricted to the traditional development mode, such as file format and other factors. With the development of computer network technology, people promote the development of graphic design by constructing color management system. At the same time, the construction of color management system can help people to change colors and define colors when they process image information and output pictures. In the process of printing pictures, in order to make the colors used in the design process clearly printed out and without color difference, there are still many problems to be considered. First, we need to consider the unexpected situation and the complexity of image processing. Based on the introduction of computer learning, this paper will discuss and study the development of graphic design by SVM theory.


1993 ◽  
Author(s):  
Masahiro Maeda ◽  
Masato Toho ◽  
Koh Kamizawa ◽  
Toru Yamasaki

1999 ◽  
Author(s):  
Takehisa Tanaka ◽  
Katsuji Aoki ◽  
Mutsuko Nichogi ◽  
Katsuhiro Kanamori

2012 ◽  
Vol 262 ◽  
pp. 31-35
Author(s):  
Sheng Yan Cai ◽  
Xuan Nie ◽  
Shu Qin Guo

In order to get optimum color reproduction results on different color devices in color management, a proper rendering intent should be selected according to color characteristics of the image. In terms of differences on color characteristics, images could be classified into synthetic graphics and natural pictures. Different rendering intent should be applied on graphics and pictures. So graphic/picture automatic classification becomes a fundamental task of color management intellectualization. Characteristics on color distribution of a large number of images have been researched in our experiments. Then it is confirmed that the essential difference between graphic and picture is the characteristics on color distribution in the neighborhood of images rather than the number of colors or the volume of image gamut. Thus, the features which have distinct ability to show the differences could be used to build classification rules. In this paper, several mathematical features of image are extracted and selected by their classification performance. Based on these features, the discriminant analysis is adopted to build up discriminated functions. Finally, the accuracy of the functions has been tested and the precision is 96.75%.


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