Since the triangle identification algorithm commonly utilized in star sensitive system is of high data redundancy and low recognition speed, especially initial recognition speed, a concentric circles-based star pattern recognition algorithm of large Field Of View (FOV) was proposed. After analyzing the information of star map to acquire its main star, draw eight concentric circles around the main star at some certain radiuses, then figure out the number of stars in each annulus based on the coordinates to obtain the distributional vector of companion stars. Construct the navigation star feature database from the base database with the utilization of the same method, so as to process the pattern matching with the distributional vector to acquire star pattern recognition result. The vectors in the database will be sorted by the first dimensional element in order to accelerate the process of recognition. The simulation results show that this algorithm needs much less storage space of navigation star feature database, and possesses good real-time and noise resistance, and high recognition rate. It takes 95.3μs recognition time to achieve more than 88.9% accuracy, and it also can be integrated with other recognition algorithms and performance in different stages to realize more efficient and accurate celestial navigation.

Image quality detection technology can automatically detection the image abnormality in order to replace manual inspection methods for the monitoring system. It can accurately analyze abnormalities of the video, and alarm the system in order to ensure normal running of the expanding network video surveillance system. Noise detection technology based on the spatial domain use the image information of the field characteristics, profile and orientation distributions of various kinds of abnormal noise in spatial domain, and take advantage of OpenCV-based image processing technology achieving detection of noise points, snowflakes and stripes. The noise detection algorithm in spatial domain proposed, is consistent with human visual perception and can be used to monitoring video for real-time detection.