scholarly journals 3D Steganalysis Using Laplacian Smoothing at Various Levels

2018 ◽  
pp. 223-232
Author(s):  
Zhenyu Li ◽  
Fenlin Liu ◽  
Adrian G. Bors
Keyword(s):  
Laplacian Smoothing

scholarly journals An Improved Laplacian Smoothing Approach for Surface Meshes

2007 ◽  
pp. 318-325
Author(s):  
Ligang Chen ◽  
Yao Zheng ◽  
Jianjun Chen ◽  
Yi Liang
Keyword(s):  
Surface Meshes ◽  
Laplacian Smoothing

scholarly journals Graph Dilated Network with Rejection Mechanism

2020 ◽  
Vol 10 (7) ◽  
pp. 2421
Author(s):  
Bencheng Yan ◽  
Chaokun Wang ◽  
Gaoyang Guo
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Great Success ◽  
Convolution Kernel ◽  
Graph Structure ◽  
Proposed Model ◽  
Rejection Mechanism ◽  
Laplacian Smoothing ◽  
Graph Neural Networks ◽  
High Level

Recently, graph neural networks (GNNs) have achieved great success in dealing with graph-based data. The basic idea of GNNs is iteratively aggregating the information from neighbors, which is a special form of Laplacian smoothing. However, most of GNNs fall into the over-smoothing problem, i.e., when the model goes deeper, the learned representations become indistinguishable. This reflects the inability of the current GNNs to explore the global graph structure. In this paper, we propose a novel graph neural network to address this problem. A rejection mechanism is designed to address the over-smoothing problem, and a dilated graph convolution kernel is presented to capture the high-level graph structure. A number of experimental results demonstrate that the proposed model outperforms the state-of-the-art GNNs, and can effectively overcome the over-smoothing problem.

scholarly journals Measuring cardiac strain using Laplacian smoothing splines

2003 ◽  
Vol 44 ◽  
pp. 249
Author(s):  
M. P. Griffin ◽  
F. Chen ◽  
K. L. McMahon ◽  
G. Campbell ◽  
S. J. Wilson ◽  
...  
Keyword(s):  
Smoothing Splines ◽  
Cardiac Strain ◽  
Laplacian Smoothing

Matching Geometric Shapes in 4D Space Incorporating Curvature Information

2007 ◽  
Author(s):  
Y. Song ◽  
J. S. M. Vergeest
Keyword(s):  
Extra Dimension ◽  
Shape Matching ◽  
Fine Tuning ◽  
Frequency Noise ◽  
Icp Algorithm ◽  
3 Dimensional ◽  
Additional Information ◽  
3D Space ◽  
Laplacian Smoothing ◽  
Curvature Information

The Iterative Closest Point (ICP) algorithm and its variants are widely used in matching different patches of 3-Dimensional (3D) scanning data. In this paper, a 4-Dimensional (4D) based approach is proposed to improve the robustness of the ICP algorithm. Considering curvatures of the given geometries as an extra dimension, the existing ICP algorithm can be extended to 4D space. The reason of using this additional information is that it introduces an extra dimension of similarity in the shape matching algorithm, thus improves the effectiveness of the optimization process. Using a variant of the Laplacian smoothing tool, high frequency noise and interferences in the curvature domain are suppressed and the principal geometric features are addressed. By a 4D to 3D orthogonal projection, the matched geometries are projected back to 3D space, where the existing ICP algorithm in 3D is applied as a fine-tuning tool. Numerical implementations on several sets of scanning data demonstrate the robustness of the proposed method. The converging process and the speed of the propose method are investigated as well.

scholarly journals Efficient Parallel Algorithms for 3D Laplacian Smoothing on the GPU

2019 ◽  
Vol 9 (24) ◽  
pp. 5437
Author(s):  
Lei Xiao ◽  
Guoxiang Yang ◽  
Kunyang Zhao ◽  
Gang Mei
Keyword(s):  
Large Scale ◽  
Graphics Processing Unit ◽  
Parallel Implementation ◽  
Three Dimensional ◽  
Smoothing Method ◽  
Three Dimensions ◽  
Processing Unit ◽  
Mesh Quality ◽  
Mesh Smoothing ◽  
Laplacian Smoothing

In numerical modeling, mesh quality is one of the decisive factors that strongly affects the accuracy of calculations and the convergence of iterations. To improve mesh quality, the Laplacian mesh smoothing method, which repositions nodes to the barycenter of adjacent nodes without changing the mesh topology, has been widely used. However, smoothing a large-scale three dimensional mesh is quite computationally expensive, and few studies have focused on accelerating the Laplacian mesh smoothing method by utilizing the graphics processing unit (GPU). This paper presents a GPU-accelerated parallel algorithm for Laplacian smoothing in three dimensions by considering the influence of different data layouts and iteration forms. To evaluate the efficiency of the GPU implementation, the parallel solution is compared with the original serial solution. Experimental results show that our parallel implementation is up to 46 times faster than the serial version.

Suspension Footbridge Form-Finding with Laplacian Smoothing Algorithm

2020 ◽  
Vol 20 (6) ◽  
pp. 1989-1995
Author(s):  
Zhuo-ju Huang ◽  
Jie-min Ding ◽  
Sheng-yi Xiang
Keyword(s):  
Smoothing Algorithm ◽  
Form Finding ◽  
Laplacian Smoothing

Improved Laplacian Smoothing of Noisy Surface Meshes

1999 ◽  
Vol 18 (3) ◽  
pp. 131-138 ◽  
Author(s):  
J. Vollmer ◽  
R. Mencl ◽  
H. Muller
Keyword(s):  
Surface Meshes ◽  
Laplacian Smoothing
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