To solve the problems of "center-surround" salient object detection and extraction method, such as incomplete object detected or extracted, not smooth boundary and redundancy caused by down-sampling 9-level pyramid, a salient object detection method based on Reciprocal Function and Spectral Residual (RFSR) was proposed. Firstly, the difference between the intensity image and its corresponding Gaussian low-pass one was used to substitute the normalization of the intensity image under "center-surround" model, meanwhile the level of Gaussian pyramid was further reduced to 6 to avoid redundancy. Secondly, a reciprocal function filter was used to extract local orientation information instead of Gabor filter. Thirdly, spectral residual algorithm was used to extract spectral feature. Finally, three extracted features were properly combined to generate the final saliency map. The experimental results on two mostly common benchmark datasets show that compared with "center-surround" and spectral residual models, the proposed method significantly improves the precision, recall and F-measure, furthermore lays a foundation for subsequent image analysis, object recognition, visual-attention-based image retrieval and so on.

The deficiency of Gaussian Mixture Model (GMM) is the high computation cost and cannot deal with the shadow and ghosting. An improved foreground detection algorithm based on GMM is proposed in this paper. By analyzing the stability of the background, intermittent or continuous frame updating is chose to update the parameters of the GMM.It can efficiently reduce the runtime of the algorithm. In the background updating,the updating rate is associated with the weight and this makes it change with the weight.The background pixels which appear after the objects moving set a larger updating rate.It can improve the stability of the background and solve the problem of ghosting phenomenon and the transformation of background and foreground.After objects detection,the algorithm eliminates the shadow based on the RGB color space distortion model and treats the result by Gauss Pyramid filtering and morphological filtering.Through the whole process,a better contour is obtained. The experimental results show that this algorithm has improved the calculation efficiency and accurately segmented the foreground object.

According to the high complexity of extracting feature of radar signal using image processing method, a new method for extracting feature was proposed. Firstly, the time-frequency distribution was gained based on the adaptive Gaussian kernel time frequency analysis, then through analyzing the physical meaning of each element, one dimension vector could be found through a simple arithmetic instead of the complicated method through processing the time-frequency figure with image processing means, so the real-time requirement for sorting radar signal could be satisfied. The simulation results verify the efficiency of the proposed algorithm. Additionally, the accuracy can be kept at a high level while the Signal-to-Noise Ratio (SNR) is low.