The moving object detection of the video image is the basis of sequence image analysis, and it is the research hot issue of today’s foreign and domestic scholars. For detecting the moving object from the scene image in time, a detection algorithm of video moving object based on Gaussian mixture models is proposed in the paper. The pixel values are seen as the combination of the foreground Gaussian distribution and the background Gaussian distribution, and the background estimation and the adaptive background update will be put up. The statistical number of the foreground pixel of the current frame determines whether the light has a larger change, and it combines with the frame-difference method to detect moving object. The experimental results show that the algorithm can quickly and accurately establish the background model and accurately segment the foreground object.

Microsymposia of PED patterns without full knowledge of the structure, a major step in realizing the goal of inverting PED data to obtain the underlying structure factor amplitudes.

On the basis of wavelet denoising and its better time-frequency characteristic, this paper presents an effective vibration signal denoising method for food refrigerant air compressor. The solution of eliminating strong noise is investigated with the combination of soft threshold and exponential lipschitza. The good denoising results show that the presented method is effective for improving the signal noise ratio and builds the good foundation for further extraction of the vibration signals.

As one of the crucial issues of computer vision, video object tracking is widely used in many applications, such as visual surveillance, human-computer interaction, visual transportation, visual navigation of robots, military guidance, etc. The existing object tracking algorithms in engineering applications have the huge amount of computation, which can not meet the needs of real-time system applications, and the tracking accuracy is not high. So a simple and practical video object tracking algorithm is proposed in this paper. The Otsu algorithm is used for image binarization to filter the background, and the object edge is further processed based on mathematical morphology, and thus the tracking object is more clearly. The centroid weighted method determines the location of the center of the object only by one step calculation, which makes the location more accurate. The experimental results show that the algorithm of the paper is effective for detecting and tracking of a moving object in a static scene and it has a low complexity.