Using unmanned aerial vehicles (UAV) for large-scale scene sampling is a prevalent application in UAV vision. However, there are certain factors that can influence the quality of UAV sampling, such as the lack of texture details and drastic changes in scene geometry. One common factor is occlusion, which is a surface feature in 3D scenes that results in significant discontinuity on the scene surface, leading to transient noise and loss of local information. This can cause degradation in the performance of computer vision algorithms. To address these challenges, this paper proposes a UAV sampling method that takes into account occlusion. The method is based on the principle of quantizing occlusion information and improves the aerial light field (ALF) technology. It establishes a UAV ALF sampling model that considers scene occlusion information and calculates the minimum sampling rate of UAV sampling by deriving the exact expression of the spectrum. The proposed model is used to sample and reconstruct large-scale scenes in different occlusion environments. Experimental results demonstrate that the model effectively improves the reconstruction quality of large-scale scenes in occluded environments.

Light field (LF) camera sensors often face a trade-off between angular resolution and spatial resolution when shooting. High spatial resolution image arrays often result in lower angular resolution, and vice versa. In order to obtain high spatial resolution and at the same time have high angular resolution. In this paper, we propose an improved 4D convolutional neural network (CNN) algorithm for angular super-resolution (SR) to improve the quality of angular SR images. Firstly, to address the problem of low luminance of images captured by LF cameras, this paper uses block threshold square reinforcement (BTSR) for image luminance enhancement. Secondly, to make the reconstructed new viewpoints of higher quality, this paper improves the attention mechanism convolutional block attention module (CBAM). This paper incorporates it into a 4D dense residual network as high dimensional attention module (HDAM). HDAM generates images along two independent dimensions, spatial and channel. The HDAM generates attention maps along two independent dimensions, space and channel, which guide the network to focus on more important features for adaptive feature modification. Finally, this paper modifies the activation function to make the network perform better in the later stages of training and more suitable for LF reconstruction tasks. This paper evaluates the network on many LF data, including real-world scenes and synthetic data. The experimental results show that the improved network algorithm can achieve higher quality LF reconstruction.

Proliferative Diabetic Retinopathy (PDR) is a seri-ous retinal disease threatening diabetic patients. Intense retinal neovascularization in the retinal image is the most important clinical symptom of PDR, leading to visual distortion if not controlled. Accurate and timely detection of neovascularization from retinal images allows patients to receive adequate treatment to avoid further vision loss. In this work, we propose a retinal neovascularization automatic segmentation model based on im-proved Pyramid Scene Parsing Network (PSP-Net). To improve the accuracy of the model, we introduce the proposed channel attention module into the model. The network is evaluated with color fundus images from practice. Evaluation results show the network is superior to FCN, SegNet, U-Net and PSP-Net in accuracy and sensitivity. The model could achieve accuracy, sensitivity, specificity, precision and Jaccard similarity score of 0.9832,0.9265,0.9897,0.9116 and 0.8501, respectively. This paper proves through plenty of experimental results that the network model is able to improve the accuracy of segmentation, relieve the workload of doctors, and is worthy of further clinical promotion.

Unmanned aerial vehicles (UAV) can capture multiview images of large-scale scenes, and then using aerial light field (ALF)-rendering technology, they can render high-quality, large-scale 3D scenes. However, the reconstruction of large-scale scenes poses challenges, such as rendering distortion and high memory consumption. In this article, we study the multiview image-capturing and novel view-rendering method of UAV sampling to address these issues. First, we present an ALF sampling model using the spectral analysis of light field and obtain an exact spectrum expression of the ALF. Through the spectral support of ALF, we determine the bandwidth and calculate the minimum required UAV sampling rate. Finally, we demonstrate that our sampling and rendering methods can improve the rendering quality of UAV 3D reconstruction and reduce the minimum sampling rate.

资金管理是集团企业财务管控的重要内容,是现代企业管理的核心.随着集团规模越来越大,子公司越来越多,管理的范围也就随之扩大,导致资金管理以及资金风险管控的内容越来越繁杂,难度越来越大,如何发挥资金规模的优势成为了衡量集团综合实力的因素.传统的资金集中管理模式已经不能满足大多数集团企业的需求,目前结算中心和内部银行等较为先进的资金管理模式都有其优缺点,财务公司模式的门槛较高,所以选择哪种资金管理模式,需要根据集团企业自身的发展战略来确定.文章从文献综述视角对目前资金管理模式进行整理分类,结合风险管理与资金管理模式之间的关系,对当前环境下现代集团企业资金集中管控提出相应的建议.