外文翻译：计算机图像处理过程及颜色分析

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外文资料

Typically, an image-processing application consists of five steps. First, an image must be acquired. A digitized representation of the image is necessary for further processing. This is denoted with a two-dimensional function I（x，y）that is described with an array. X marks a column and y a row of the array. The domain for x and y depends on the maximal resolution of the image. If the image has size n ⨯m, whereby n represents the number of rows and m the number of columns, then it -holds for x that 0 ≤x

is the and y are positive integers or zero. This holds also for the domain of I（x，y）

mas maximal value for the function value. This then provides the domain, 0 ≤I(x, y) ≤I(x;

. Every possible discrete function value represents a gray value and is called a pixel.

y)

max

Subsequent preprocessing tries to eliminate disturbing effects. Examples are inhomogeneous illumination noise, and movement detection.

If image-preprocessing algorithms like the movement detection are applied to an image, it is possible that image pixels of different objects with different properties are merged into regions, because they fulfill the criteria of the preprocessing algorithm. Therefore, a region can be considered as the accumulation of coherent pixels that must not have any similarities. These image regions or the whole image can be decomposed into segments. All contained pixels must be similar in these segments.

Pixels will be assigned to objects in the segmentation phase, which is the third step. If objects are isolated from the remainder of the image in the segmentation phase, feature values of these objects must be acquired in the fourth step. The features determined are used in the fifth and last step to perform the classification. This means that the detected objects are allocated to an object class if their measured feature values match to the object description. Examples for features are the object height, object width, compactness, and circularity.

A circular region has the compactness of one. The alteration of the region‘s length effects the alteration of the compactness value. The compactness becomes larger if the region‘s length rises. An empty region has value zero for the compactness.

Color Models

The process of vision by a human being is also controlled by colors. This happens subconsciously with signal colors. But a human being searches in some situations directly for specified colors to solve a problem. The color attribute of an object can also be used in computer vision. This knowledge can help to solve a task .For example, a computer-vision application that is developed to detect people can use knowledge about the color of the skin