外文翻译---关于颜色识别中颜色特征分析的方法

外文翻译---关于颜色识别中颜色特征分析的方法内容摘要：

on can be performed from RGB space. Transformation from RGB space to YUV space can beexplained approximated by the following equations: D. HSI Color Space HSI space is established from the human psychologicalperception point of view. H (hue) is a color in a color corresponding to the main wavelength in chromatography. S (saturation) is equivalent to the purity of color. I (intensity ) is the brightness of color and the uniform amount of feeling. HSV (hue, saturation, value) and HSB (hue, saturation, brightness) are other color spaces similar to HSI color space , and are all belong to polar coordinate space structure. Their mon merit is that they can describe the color intuitively. Most of them can be converted from RGB space linearly. HSI color space has two important points. One is that I ponent is separated from H ponent, . I ponent is independent of image color information. The other is that H ponent and S ponent are closely linked to the way human feeling color, where the color description ability of H ponent is the most closet to human vision. And then distinguish ability of H ponent is the strongest [8]. Transformation from RGB space to HSI space can be explained by the following equations: 陕西科技大学 5 HSI color space provides a suitable space with three ponents that is better used to descript color in line with human hobbits. However, the defect of nonlinear in color difference still exists, especially the color and angle in the H ponent [9]. E. I1I2I3 Color Space Linear transformation from RGB space to I1I2I3space can be explained by the following equations to get three orthogonal color features: From formula (7), it can be seen that values of I1, I2and I3 ponent can be positive and negative. The noncorrelation property of I1I2I3 space is the best in image recognition. III. FEATURE EVALUATION OF COLOR SPACE By color spaces, the abstract, subjective visual perception can be translated into a concrete specific position, vector in threedimensional space, which makes it possible to visualize color features of colorful images and devices. Color space is an important tool of color recognition. Various mixing system has its corresponding color space, and different color spaces have different properties with their respective advantages and disadvantages. Validity of color space is the key to color image processing. Divisibility criterion can be used to test different color space for their performance on color classification. The distance criterion is widely utilized due to its concise and clear concept. Its principle 陕西科技大学 6 is that the smaller the distance within a class while the greater the distance between classes, the better the divisibility it has. Below is the presented algorithm of feature evaluation based on distance criterion [10]. • Calculate the mean vector and covariance of the ith class samples, N is the number of total samples and Nithe number of the ithclass samples. 陕西科技大学 7 IV. E X。