A Fast Luminance Inspector for Backlight Modules Based on Multiple Kernel Support Vector Regression

The liquid crystal display (LCD) is widely used in various devices nowadays, for examples, mobile phones, digital cameras, and machine controllers. Among the components composing an LCD, the backlight module is one of the most important components because it provides a uniform light source for the display. In the current practice of backlight module inspection, a luminance colorimeter is used to examine the luminance emitted from selected areas called check points within the backlight module. Since a backlight module consists of many check points, it takes too much time to inspect one backlight module in the current practice. In this paper, we propose a fast luminance inspector that uses a CCD camera to inspect multiple backlight modules, simultaneously. The inspection speed is improved considerably, and thus, makes the proposed system suitable for use in the production line. The proposed system translates image intensities of all check points into luminance values based on a multiple kernel support vector regression model (MKSVR). The parameters of multiple kernel functions are automatically generated, according to the data distribution characteristics of the training samples. Compared with other studies using a grid search or a heuristic search algorithm to determine optimal kernel parameters, the proposed approach is more flexible and faster. Besides that, as the kernel parameters are adaptive to the training data, the proposed method could make its learning more specific to certain golden training samples. Other methods using learning algorithms such as the neural network and the SVR do not have such flexibility, because in these learning algorithms all training samples are treated equally. The flexible learning property makes the proposed system more appealing for use in the real practical world. Compared with other methods using the neural network model and the SVR model in backlight module inspection, the proposed method is superior in both the accuracy and the training time required.