This paper puts forward a complete track forecasting models, using Kalman filter to track and predict the movement of objects without prior knowledge. Use the extracted Harris corner to calculate optical flow between two frames by L-K pyramid method, getting the convex hull of moving objects by optical flow clustering to separate the moving objects from background. Tracking and predicting the gravity of moving objects convex hull can solve the occlusion and separation problem between moving objects. Computer simulation and field test results show that the proposed method has higher tracking accuracy, and small amount of calculation.

The visual quality of medical images is an important aspect in PACS implementation. In this study, on the basis of wavelet analysis, a denoising and enhancement algorithm for medical image is proposed. The algorithm mainly includes six steps. At first, an effcient method is investigated for Poisson Noise remove. Second, diagnosis features of the denoised image are enhanced by compressing the dynamic range. Third, we extract the high frequency component of the original image by the designed lowpass filter. Fourth, the extracted high frequency component are segment into diagnosis feature component in the high signal range, the diagnosis feature component in the low signal range, and the noise component. Five, we reconstruct an image using image fusion. Finally, we make DICOM calibration for used display and decide parameters of the image fusion, resulting in the diagnosis image. Experimental results show that this new scheme offers effective noise removal in medical images and enhancing sharpness. More importantly, this scheme can improve the diagnostic value of the display image on the commercial display successfully.