Stereoscopic correspondence matching is applied in many applications like robot navigation, automatic driving, virtual, and augmented reality by reconstructing the scene in three-dimensional environments. In the most real scenes, the moving objects attract more attentions than static objects and background. Thus, temporal information of consecutive frames like motion flow has been proven to improve the matching accuracy as weight prior. In this article, we propose a costaggregation method joining object flow and minimum spanning tree-based support region rather than aggregating on fixed size windows. However, directly combining object flow and minimum spanning tree filtering is not straightforward, due to the extremely high computing complexity. The proposed scheme implements nonlocal cost aggregation with object-based optical flow, which extends the idea of the minimum spanning tree and flow-based motion estimation to increase the matching accuracy. Temporal evidence of object flow is not only used in minimum spanning tree support region building but also incorporated with one hybrid edge prior to optimize the disparity estimation. The experimental results with synthetic stereo videos show that the proposed method outperforms other state-of-the-art stereo matching methods in most data sets. The whole stereo correspondence algorithm achieves competitive performance in terms of both accuracy and speed. We also illustrate the performance of the proposed method with the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) benchmark as one extensive comparison.
Background: The National Natural Science Foundation of China (NSFC) is a vital source of support for scientific research in China and abroad. Among the 18 categories programs managed by the NSFC, the Joint Fund, which looks to develop innovation in basic Chinese research, has drawn particular interest from scientists and researchers. This study set out to review the distribution of funding from the Joint Fund in recent years to provide potential applicants with a useful point of reference.Methods: Guidebooks and annual reports published from 2015 to 2019 were accessed on the NSFC website and the relevant data from the Joint Fund were obtained. The data were analyzed to identify any trends or patterns in funding distribution.Results: Between 2015 and 2019, the NSFC's Joint Fund awarded a total of 46 million Chinese yuan (CNY) to 354 projects in 29 areas within the field of population health. The funding recipients, who were aged between 30 and 66 years old, came from 47 host institutions. Among the successful applicants, 225 were in senior level positions, 129 were in deputy senior level positions, and 19 were in medium-grade professional positions. Collaboration between institutions was a common feature of funding applications.Conclusions: By analyzing the distribution of funding from the Joint Fund, we identified a number of common features of successful funding applications, which may help to inform future funding applications.
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