Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Liu, Ye; Coombes, Matthew; Liu, Cunjia

doi:10.1109/tsipn.2023.3278469

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…Mesh surface filtering [1][2][3][4][5][6][7] is a pivotal process in the domain of computer graphics and computational geometry, aimed at enhancing the visual quality and computational efficiency of three-dimensional (3D) mesh models. A 3D mesh is a discrete representation of a surface composed of vertices, edges, and faces.…”

Section: Introductionmentioning

confidence: 99%

Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods

BEŞDOK,

ÇİVİCİOĞLU

2023

Mersin Photogrammetry Journal

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Mesh filtering of surfaces is crucial for noise reduction, feature preservation, and mesh simplification in graphics, visualization, and computer vision. In this paper, the detail preservation capacities of 3 frequently used filters, i.e., Bilateral, Laplacian, and Taubin mesh filters, in mesh filtering have been thoroughly examined by experiments conducted on 4 different test meshes. Bilateral filtering maintains sharp features by combining geometric closeness and intensity resemblance but may be computationally intensive. Laplacian filtering smoothens the surface by averaging neighboring vertices but might over-smooth sharp details, while Taubin filtering iteratively applies Laplacian and high-pass filters, achieving smoothness without shrinking the mesh. The statistical analysis of the experimental results has shown that the Taubin method is statistically a more successful mesh filtering method for the test sets used in this paper.

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Section: Introductionmentioning

confidence: 99%

Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods

BEŞDOK,

ÇİVİCİOĞLU

2023

Mersin Photogrammetry Journal

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A Novel Depth-Connected Region-Based Convolutional Neural Network for Small Defect Detection in Additive Manufacturing

Wang,

Liu

et al. 2024

Cogn Comput

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Defect detection on the computed tomography (CT) images plays an important role in the development of metallic additive manufacturing (AM). Although some deep learning techniques have been adopted in the CT image-based defect detection problem, it is still a challenging task to accurately detect small-size defects in the presence of undesirable noises. In this paper, a novel defect detection method, namely, the depth-connected region-based convolutional neural network (DC-RCNN), is proposed to detect small defects and reduce the influence of noises. In particular, a saliency-guided region proposal method is first developed to generate small-size region proposals with the aim to accommodate the small defects. Then, the main architecture of DC-RCNN is proposed to extract and connect the consistent features across multiple frames, thereby reducing the influence of randomly distributed noises. Moreover, the transfer learning technique is utilized to improve the generalization ability of the proposed DC-RCNN. In order to verify the effectiveness and superiority, the proposed method is applied to the real-world AM data for defect detection. The experimental validations show that the proposed DC-RCNN is able to detect the small-size defects under noises and outperforms the original RCNN method in terms of detection accuracy and running time.

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Encoding-Decoding-Based Distributed Fusion Filtering for Multi-Rate Nonlinear Systems With Sensor Resolutions

Hu,

Fan,

Chen

et al. 2023

IEEE Trans. on Signal and Inf. Process. over Networks

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Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Cited by 4 publications

References 39 publications

Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods

Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods

A Novel Depth-Connected Region-Based Convolutional Neural Network for Small Defect Detection in Additive Manufacturing

Encoding-Decoding-Based Distributed Fusion Filtering for Multi-Rate Nonlinear Systems With Sensor Resolutions

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