外文翻譯---關(guān)于顏色識別中顏色特征分析的方法-在線瀏覽
2025-01-05 08:06本頁面
【正文】 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: 陜西科技大學(xué) 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 陜西科技大學(xué) 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. 陜西科技大學(xué) 7 IV. E XPERIMENTAL RESULTS AND ANALYSIS When identified by human eyes, colors are divided into eleven categories as red, green, blue, yellow, purple, orange, pink, brown, gray, white and black, shown in Fig. 2. The evaluation algorithm is performed respectively on RGB space, CMY space, YUV space, I1I2I3 spaceand HSI space. Feature parameter and assessment indicators are shown in Table Ⅰ . 陜西科技大學(xué) 8 Seen from Table Ⅰ , the HSI space has the best performance pared to other four analyzed color spaces. V. CONCLUSION It is necessary to select an effective color space for colorful image processing. This paper analyzes and pares the p
公司管理相關(guān)推薦
鄂ICP備17016276號-1