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As one of the most important features of human face, the eye location plays a fundamental role in many application fields. For example, by judging the eyes state, defining whether the driver is in the state of fatigue. At present, most of the eye detection algorithms in fatigue driving are based on laboratory simulation environment. Therefore, this paper is based on the real car environment, collecting the pictures from the driver simulating the driving condition. Then, using the method of region segmentation and ellipse fitting to locate and analyze the state of eyes. All experiment pictures are collected by Android phone front camera. The algorithm runs fast and is suitable for real-time processing. At the same time, there is a good performance of eye location in the situation where there exists a certain facial orientation angle.

Oil spill may cause serious pollution of the marine environment. Unmanned aerial vehicles remote sensing system can be used to monitor oil spill conditions. In order to identify the oil spill area on aerial image accurately, the first step is oil spill region segmentation. The paper presents an image segmentation method of oil spill area based on fuzzy C-means Algorithm. Firstly, according to the color characteristics of the oil, the paper selects YCbCr color space as the feature space. Then, the paper uses fuzzy clustering algorithm to divide the color feature space. Finally, according to oil color model, the paper selects clustering result as the segmentation results of oil spill area. Experiment show that the proposed algorithm’s accuracy for oil region segmentation of calibration attain to 95 percent. Keywords—Oil aerial image; color model; YCbCr color space; fuzzy C-means Algorithm