Objective: With data drawn from Beijing’s New Rural Cooperative Medical System (NRCMS), the rule characteristics of disease risks are mined in terms of risk factors and risk measurements aiming to discover valuable knowledge within the vast amounts of Beijing’s NRCMS data and provide administrators with a more scientific basis for decision making. Methodology:The association rule algorithm is utilized to recover both potentially valuable knowledge and decision-making information from Beijing’s NRCMS data. Results: The main objects of healthcare in Beijing from 2012 to 2014 include: circulatory diseases in patients 41 years of age or older, pediatric respiratory disease prevention, reproductive healthcare for women of childbearing age, and the prevention and treatment of diabetes in female patients; in county-level hospitals with a relatively low average level of consumption, injuries still resulted in high expenses; the primary post-NRCMS reimbursement level-3 and level-4 high-risk groups were patients of 41–65 years of age. Conclusion: According to the ranking of rule supports, the highest support rule in Beijing is the circulatory diseases of middle-aged patients, especially patients that are hospitalized in county-level medical institutions; the second highest support comes from the utilization of fertility services for women of childbearing age. Suggestions: It is recommended that the rule support rankings should be combined to actively and implement with emphasis major prevention and healthcare services, lower disease risk factors, control NRCMS reimbursement costs, promote the sustainable development of NRCMS, adopt a classified management of mined rules, and establish a decision-making knowledge base.

This paper proposes a quasi-dense matching approach to the automatic acquisition of camera parameters, which is required for recovering 3-D information from 2-D images. An affine transformation-based optimization model and a new matching cost function are used to acquire quasi-dense correspondences with high accuracy in each pair of views. These correspondences can be effectively detected and tracked at the sub-pixel level in multiviews with our neighboring view selection strategy. A two-layer iteration algorithm is proposed to optimize 3-D quasi-dense points and camera parameters. In the inner layer, different optimization strategies based on local photometric consistency and a global objective function are employed to optimize the 3-D quasi-dense points and camera parameters, respectively. In the outer layer, quasi-dense correspondences are resampled to guide a new estimation and optimization process of the camera parameters. We demonstrate the effectiveness of our algorithm with several experiments.

Video foreground prediction is a technique to estimate the probability of each pixel being foreground in current frame based on a foreground segmentation result of its previous frame. Existing foreground prediction algorithms usually assume that the illumination conditions are constant for consecutive frames. Therefore, they cannot predict foreground accurately when the illumination condition changes sharply between video frames. In this paper, a new robust video foreground prediction algorithm is proposed based on color recovering, which is derived based on an observation that the illumination changes are locally smooth. By integrating color recovering with an optical flow estimation algorithm and an opacity propagation algorithm, the negative impact of the illumination changes could be removed. Experimental results show that the proposed algorithm can get more accurate results for videos with illumination changes compared with the existing foreground prediction algorithms.

Image composition is a very important technique in computer generated imagery. Besides some factors such as contrast, texture and noise that affect the quality of the composition, color harmony between fore- and background is also an important factor that would affect the quality of the composition. However, in the previous image composition techniques, color harmony between fore- and background is seldom considered. In this paper, an optimization method is proposed to deal with the color harmonization problem that used in image composition. A cost function is derived from the local smoothness of the hue values, and the image is harmonized by minimizing the cost function. A new matching cost function is proposed to select the best matching harmonic schemes. Our approach overcomes several shortcomings of the existing color harmonization methods. We validate the performance of our method and demonstrate its effectiveness with a variety of experiments.

This paper presents a new approach to reconstruct 3D dense point cloud from uncalibrated wide-baseline images. It includes three steps: acquiring quasi-dense point correspondences, recovering structure from motion, and reconstructing the 3D dense point cloud. We present a two level propagation algorithm. The first level is implemented in the 2D image space and the second level is in the 3D scene space. We use affine iterative model to acquire accurate quasi-dense correspondences, and iterative optimization to recover the camera parameters and the 3D scene structure. It increases the robust and accuracy of self-calibration. In the second level propagation, the strategy of view selection and local photometric consistency are used to minimize the effects of occlusions and highlights etc. We demonstrate our algorithm with some high-quality reconstruction examples.

In this paper, an automatic foreground extraction algorithm for images and videos is presented. It first automatically locates the foreground object coarsely by a salience detection algorithm, and then refines the object by Weighted Kernel Density Estimation (WKDE) and graph cut algorithm. A new initial probability map construction algorithm for WKDE is also presented. We then extend our algorithm to videos by an opacity tracking algorithm. It can automatically extract the foreground with high accuracy even for the videos with non-static background. The experiments demonstrate the effectiveness of our approach.

This paper presents a robust and accurate self-calibration approach from unordered wide-baseline images. An optimization model based on affine transformation is introduced into propagation to acquire higher accuracy of quasi-dense correspondences. Our self-calibration algorithm is completed through two-layer iteration. In the inner layer, global objective function and local photometric consistency is used to iteratively optimize camera parameters and scene structure. In the outer layer, a resampling strategy is presented to iteratively select a group of quasi-dense correspondences to solve the camera parameters and scene structure. We demonstrate our algorithm with several high accurate calibration results.

In this paper, a quasi-automatic video matting approach which can preserve the temporal consistency of the alpha mattes is presented. “Quasi-automatic” means that it only needs a few user interactions on the first frame. A new algorithm which incorporates the Bayesian Estimation, Weighted Kernel Density Estimation (WKDE) and graph cut is presented to automatically and accurately segment each frame into bilayer. And then a GMM (Gaussian Mixture Model) based algorithm is used to transform the foreground layer to the trimap. At last, a 3D closed-form method is used to achieve the temporally consistent video matting. In order to give the user real time feedbacks when the user adds more scribbles to edit the matting result, a smallest-independent-region based re-matting algorithm is proposed. We test our algorithm with variety of video sequences, and the results demonstrate the effectiveness of our approach.

Ghost is an important problem in the process of image mosaic. It results from the inaccurate registration or the moving objects in the image. In order to get rid of the ghost coming from inaccurate registration effectively, phase correlation technique, median flow filter and improved RANSAC are used to improve the accuracy of local registration. Bundle adjustment is used to correct the accumulated errors caused by the concatenation of pairwise homographies and the disregarded multiple constraints between images. In this paper, we put forward a new blending method which searches the blending region dynamically to eliminate the ghost caused by moving objects. Experiments demonstrate our method effective.