shape context
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Author(s):  
Daiping Wei ◽  
Xiaofeng Liu ◽  
Bangxin Wang ◽  
Zhi Tang ◽  
Lin Bo

Abstract Lamb waves were utilized to quantify micro-crack damage in aluminum plates, and the scattering and mode conversion of Lamb waves passing through cracks were analyzed. The dynamic time warping (DWT) method was used to match and compare each Lamb wave time series that represented different damage degrees. The matching difference between the damaged plate and undamaged plate was taken as a marker to measure the damage degree of the workpiece. At the same time, due to the pathological alignment of traditional DTW methods, the shape context (SC) profile recognition method was introduced to optimize the algorithm for calculating the distance between sampling points in the DTW method and solve the pathological alignment problem. Finally, the SC-DTW method based on Lamb waves was verified by the finite element simulation model and bending test of aluminum plates. The results showed that the method was feasible for quantifying the damage degree of aluminum plates and had a great advantage in the analysis and processing of time series in low-sampling frequency and high-noise scenarios.


Author(s):  
Joao Paulo Dias ◽  
Ariful Bhuiyan ◽  
Nabila Shamim

Abstract An estimated number of 300,000 new anterior cruciate ligament (ACL) injuries occur each year in the United States. Although several magnetic resonance (MR) imaging-based ACL diagnostics methods have already been proposed in the literature, most of them are based on machine learning or deep learning strategies, which are computationally expensive. In this paper, we propose a diagnostics framework for the risk of injury in the anterior cruciate ligament (ACL) based on the application of the inner-distance shape context (IDSC) to describe the curvature of the intercondylar notch from MR images. First, the contours of the intercondylar notch curvature from 91 MR images of the distal end of the femur (70 healthy and 21 with confirmed ACL injury) were extracted manually using standard image processing tools. Next, the IDSC was applied to calculate the similarity factor between the extracted contours and reference standard curvatures. Finally, probability density functions of the similarity factor data were obtained through parametric statistical inference, and the accuracy of the ACL injury risk diagnostics framework was assessed using receiver operating characteristic analysis (ROC). The overall results for the area under the curve (AUC) showed that method reached a maximum accuracy of about 66%. Furthermore, the sensitivity and specificity results showed that an optimum discrimination threshold value for the similarity factor can be pursued that minimizes the incidence of false positives and false positives simultaneously.


Author(s):  
Hang Liu ◽  
Tiefeng Ma ◽  
Yi Zhang ◽  
Shuangzhe Liu ◽  
Youran Wang ◽  
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2021 ◽  
Vol 11 (8) ◽  
pp. 3426
Author(s):  
Guangxuan Xu ◽  
Yajun Pang ◽  
Zhenxu Bai ◽  
Yulei Wang ◽  
Zhiwei Lu

Point clouds registration is an important step for laser scanner data processing, and there have been numerous methods. However, the existing methods often suffer from low accuracy and low speed when registering large point clouds. To meet this challenge, an improved iterative closest point (ICP) algorithm combining random sample consensus (RANSAC) algorithm, intrinsic shape signatures (ISS), and 3D shape context (3DSC) is proposed. The proposed method firstly uses voxel grid filter for down-sampling. Next, the feature points are extracted by the ISS algorithm and described by the 3DSC. Afterwards, the ISS-3DSC features are used for rough registration with the RANSAC algorithm. Finally, the ICP algorithm is used for accurate registration. The experimental results show that the proposed algorithm has faster registration speed than the compared algorithms, while maintaining high registration accuracy.


Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 499 ◽  
Author(s):  
Chaoyan Zhang ◽  
Yan Zheng ◽  
Baolong Guo ◽  
Cheng Li ◽  
Nannan Liao

Shape classification and matching is an important branch of computer vision. It is widely used in image retrieval and target tracking. Shape context method, curvature scale space (CSS) operator and its improvement have been the main algorithms of shape matching and classification. The shape classification network (SCN) algorithm is proposed inspired by LeNet5 basic network structure. Then, the network structure of SCN is introduced and analyzed in detail, and the specific parameters of the network structure are explained. In the experimental part, SCN is used to perform classification tasks on three shape datasets, and the advantages and limitations of our algorithm are analyzed in detail according to the experimental results. SCN performs better than many traditional shape classification algorithms. Accordingly, a practical example is given to show that SCN can save computing resources.


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