TPNet: A novel mesh analysis method via topology preservation and perception enhancement

Liu, Peifang; He, Fazhi; Fan, Bo; Song, Yupeng

doi:10.1016/j.cagd.2023.102219

Cited by 29 publications

(3 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, exploring more elaborate mesh segmentation methods may achieve better simplification results. Third, adapting neural networks to 3D mesh processing is worth studying [37][38][39].…”

Section: Discussionmentioning

confidence: 99%

Delaunay meshes simplification with multi‐objective optimisation and fine tuning

Fan,

Wu,

et al. 2023

IET Collab Intel Manufact

Self Cite

View full text Add to dashboard Cite

Abstract3D meshes simplification plays an important role in many industrial domains. The two goals of Delaunay mesh simplification are maintaining high geometric fidelity and reducing mesh complexity. However, they are conflicting and cannot solved by gradient. Such limitation prevents existing Delaunay mesh simplification to obtain a small enough number of vertices and promising fidelity at the same time. To address these issues, this paper proposes an evolutionary multi‐objective approach for Delaunay mesh simplification. Firstly, the authors replace the previous fixed error‐bound threshold by the designed adaptive segment‐specific thresholds. Secondly, a constrained simplification is performed through a series of edge collapses that satisfy both Delaunay and error constraints. Next, the non‐dominated sorting genetic algorithm II (NSGA‐II) is employed to solve the multi‐objective problem to search for the optimal trade‐off threshold sequences. Finally, a fine‐tuning method is designed to further enhance the geometric fidelity of the simplified mesh. Experimental results demonstrate that the authors’ method consistently achieves a satisfactory balance between the approximation error and number of vertices, outperforming existing state‐of‐the‐art methods.

show abstract

“…Second, exploring more elaborate mesh segmentation methods may achieve better simplification results. Third, adapting neural networks to 3D mesh processing is worth studying [37][38][39].…”

Section: Discussionmentioning

confidence: 99%

Delaunay meshes simplification with multi‐objective optimisation and fine tuning

Fan,

Wu,

et al. 2023

IET Collab Intel Manufact

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, Deep learning-based models can provide better results compared to these traditional methods, because deep models gives better visual quality by extracting various features of an image as example Li et al proposed TP-Net [30] as 3D shape classification and segmentation tasks, on a wide range of common datasets, which main contribution is the design of dilated convolution strategy tailored for the irregular and non-uniform structure of 3D mesh data.…”

Section: Related Workmentioning

confidence: 99%

Ultrasound breast images denoising using generative adversarial networks (GANs)

Jiménez-Gaona,

Rodríguez-Alvarez,

Escudero

et al. 2024

IDA

View full text Add to dashboard Cite

INTRODUCTION: Ultrasound in conjunction with mammography imaging, plays a vital role in the early detection and diagnosis of breast cancer. However, speckle noise affects medical ultrasound images and degrades visual radiological interpretation. Speckle carries information about the interactions of the ultrasound pulse with the tissue microstructure, which generally causes several difficulties in identifying malignant and benign regions. The application of deep learning in image denoising has gained more attention in recent years. OBJECTIVES: The main objective of this work is to reduce speckle noise while preserving features and details in breast ultrasound images using GAN models. METHODS: We proposed two GANs models (Conditional GAN and Wasserstein GAN) for speckle-denoising public breast ultrasound databases: BUSI, DATASET A, AND UDIAT (DATASET B). The Conditional GAN model was trained using the Unet architecture, and the WGAN model was trained using the Resnet architecture. The image quality results in both algorithms were measured by Peak Signal to Noise Ratio (PSNR, 35–40 dB) and Structural Similarity Index (SSIM, 0.90–0.95) standard values. RESULTS: The experimental analysis clearly shows that the Conditional GAN model achieves better breast ultrasound despeckling performance over the datasets in terms of PSNR = 38.18 dB and SSIM = 0.96 with respect to the WGAN model (PSNR = 33.0068 dB and SSIM = 0.91) on the small ultrasound training datasets. CONCLUSIONS: The observed performance differences between CGAN and WGAN will help to better implement new tasks in a computer-aided detection/diagnosis (CAD) system. In future work, these data can be used as CAD input training for image classification, reducing overfitting and improving the performance and accuracy of deep convolutional algorithms.

show abstract

“…Artificial neural networks present a promising outcome for the modeling of complex systems or those with unknown specifications [27]. Recent emerging techniques such as the 3D mesh learning method [28] and perceptual metric-guided generative adversarial networks [29] have improved their efficacy further. The development of a neural network identifier is outlined in [30], which operates in the complex domain for uncertain nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Fractional Artificial Neural Networks for Modeling Cancer Cell Behavior

Behinfaraz,

Ghavifekr,

De Fazio

et al. 2023

Electronics

View full text Add to dashboard Cite

In this paper, a novel approach involving a fractional recurrent neural network (RNN) is proposed to achieve the observer-based synchronization of a cancer cell model. According to the properties of recurrent neural networks, our proposed framework serves as a predictive method for the behavior of fractional-order chaotic cancer systems with uncertain orders. Through a stability analysis of weight updating laws, we design a fractional-order Nonlinear Autoregressive with Exogenous Inputs (NARX) network, in which its learning algorithm demonstrates admissible and faster convergence. The main contribution of this paper lies in the development of a fractional neural observer for the fractional-order cancer systems, which is robust in the presence of uncertain orders. The proposed fractional-order model for cancer can capture complex and nonlinear behaviors more accurately than traditional integer-order models. This improved accuracy can provide a more realistic representation of cancer dynamics. Simulation results are presented to demonstrate the effectiveness of the proposed method, where mean square errors of synchronization by applying integer and fractional weight matrix laws are calculated. The density of tumor cell, density of healthy host cell and density of effector immune cell errors for the observer-based synchronization of fractional-order (OSFO) cancer system are less than 0.0.0048, 0.0062 and 0.0068, respectively. Comparative tables are provided to validate the improved accuracy achieved by the proposed framework.

show abstract

TPNet: A novel mesh analysis method via topology preservation and perception enhancement

Cited by 29 publications

References 51 publications

Delaunay meshes simplification with multi‐objective optimisation and fine tuning

Delaunay meshes simplification with multi‐objective optimisation and fine tuning

Ultrasound breast images denoising using generative adversarial networks (GANs)

Utilizing Fractional Artificial Neural Networks for Modeling Cancer Cell Behavior

Contact Info

Product

Resources

About