projection filtering
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2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Aihua Pu ◽  
Hua Wang ◽  
Jichong Ying

To explore the computed tomography (CT) imaging characteristics and BPF algorithm fine lung CT image efficiency for the diagnosis of pelvic fracture patients and assist clinicians to carry out the disease care and treatment, CT images based on optimized back-projection filtering (BPF) algorithm were utilized to diagnose postoperative reduction of pelvic fractures and penetrating lung infection caused by long-term bed rest. A total of 100 patients with pelvic fracture were selected and all of them underwent pelvic fracture surgery and were rolled into conventional CT diagnosis group (conventional group) and BPF algorithm optimized CT image diagnosis group (BPF group). One group used conventional CT images to guide pelvic reduction and detect lung infections, and the other used BPF algorithm to optimize the images. The results showed that the BPF group was superior to the conventional CT group in both image clarity and shadow area, and the peak signal-to-noise ratio (PSNR) was significantly better than that of the conventional group ( P < 0.05 ). Nine more cases were detected in the algorithm group than in the conventional group, and the incidence of complications was 48% in the conventional group and 28% in the BPF group, with a statistical difference of 20% between the two groups ( P < 0.05 ). In addition, the satisfaction of returning patients was 96% in the BPF group and 77% in the conventional group ( P < 0.05 ). The diagnosis of pulmonary infection was more obvious in the BPF group, indicating that BPF optimization of the CT image was suitable for clinical diagnosis and had a practical application value.


Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Lijuan Chen ◽  
Zihao Zhang ◽  
Yapeng Zhang ◽  
Xiaoshuang Xiong ◽  
Fei Fan ◽  
...  

For non-linear systems (NLSs), the state estimation problem is an essential and important problem. This paper deals with the nonlinear state estimation problems in nonlinear and non-Gaussian systems. Recently, the Bayesian filter designer based on the Bayesian principle has been widely applied to the state estimation problem in NLSs. However, we assume that the state estimation models are nonlinear and non-Gaussian, applying traditional, typical nonlinear filtering methods, and there is no precise result for the system state estimation problem. Therefore, the larger the estimation error, the lower the estimation accuracy. To perfect the imperfections, a projection filtering method (PFM) based on the Bayesian estimation approach is applied to estimate the state. First, this paper constructs its projection symmetric interval to select the basis function. Second, the prior probability density of NLSs can be projected into the basis function space, and the prior probability density solution can be solved by using the Fokker–Planck Equation (FPE). According to the Bayes formula, the proposed estimator utilizes the basis function in projected space to iteratively calculate the posterior probability density; thus, it avoids calculating the partial differential equation. By taking two illustrative examples, it is also compared with the traditional UKF and PF algorithm, and the numerical experiment results show the feasibility and effectiveness of the novel nonlinear state estimation filter algorithm.


Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3054 ◽  
Author(s):  
Ciyun Lin ◽  
Hui Liu ◽  
Dayong Wu ◽  
Bowen Gong

A light detection and ranging (LiDAR) sensor can obtain richer and more detailed traffic flow information than traditional traffic detectors, which could be valuable data input for various novel intelligent transportation applications. However, the point cloud generated by LiDAR scanning not only includes road user points but also other surrounding object points. It is necessary to remove the worthless points from the point cloud by using a suitable background filtering algorithm to accelerate the micro-level traffic data extraction. This paper presents a background point filtering algorithm using a slice-based projection filtering (SPF) method. First, a 3-D point cloud is projected to 2-D polar coordinates to reduce the point data dimensions and improve the processing efficiency. Then, the point cloud is classified into four categories in a slice unit: Valuable object points (VOPs), worthless object points (WOPs), abnormal ground points (AGPs), and normal ground points (NGPs). Based on the point cloud classification results, the traffic objects (pedestrians and vehicles) and their surrounding information can be easily identified from an individual frame of the point cloud. We proposed an artificial neuron network (ANN)-based model to improve the adaptability of the algorithm in dealing with the road gradient and LiDAR-employing inclination. The experimental results showed that the algorithm of this paper successfully extracted the valuable points, such as road users and curbstones. Compared to the random sample consensus (RANSAC) algorithm and 3-D density-statistic-filtering (3-D-DSF) algorithm, the proposed algorithm in this paper demonstrated better performance in terms of the run-time and background filtering accuracy.


Author(s):  
X. Liu ◽  
Y. Zhang ◽  
Q. Li

Pedestrian crossing, as an important part of transportation infrastructures, serves to secure pedestrians’ lives and possessions and keep traffic flow in order. As a prominent feature in the street scene, detection of pedestrian crossing contributes to 3D road marking reconstruction and diminishing the adverse impact of outliers in 3D street scene reconstruction. Since pedestrian crossing is subject to wearing and tearing from heavy traffic flow, it is of great imperative to monitor its status quo. On this account, an approach of automatic pedestrian crossing detection using images from vehicle-based Mobile Mapping System is put forward and its defilement and impairment are analyzed in this paper. Firstly, pedestrian crossing classifier is trained with low recall rate. Then initial detections are refined by utilizing projection filtering, contour information analysis, and monocular vision. Finally, a pedestrian crossing detection and analysis system with high recall rate, precision and robustness will be achieved. This system works for pedestrian crossing detection under different situations and light conditions. It can recognize defiled and impaired crossings automatically in the meanwhile, which facilitates monitoring and maintenance of traffic facilities, so as to reduce potential traffic safety problems and secure lives and property.


2017 ◽  
Vol 11 (8) ◽  
pp. 1228-1234 ◽  
Author(s):  
Kwanggoo Yeo ◽  
Youngseek Chung ◽  
Hoongee Yang ◽  
Jongmann Kim ◽  
Wonzoo Chung

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