Duong Q. Nguyen¹, Thinh D. Le³, Phuong D. Nguyen³, Nga T. K. Le², H. Nguyen-Xuan^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2197-2214, 2024, DOI:10.32604/cmes.2023.043992

Abstract Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications. In this paper, we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network. Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions. To achieve accurate segmentation, we conducted thorough experiments and selected a high-performing model from the trained models. The selected model demonstrates exceptional segmentation performance for complex 3D facial wounds. Furthermore, based on the segmentation model, we propose an improved approach for extracting… More > Graphic Abstract

Jee-Sic Hur¹, Hyeong-Geun Lee¹, Shinjin Kang², Yeo Chan Yoon³, Soo Kyun Kim^1,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6213-6227, 2023, DOI:10.32604/cmc.2023.035344

Abstract In this paper, we proposed a combined PCA-LPP algorithm to improve 3D face reconstruction performance. Principal component analysis (PCA) is commonly used to compress images and extract features. One disadvantage of PCA is local feature loss. To address this, various studies have proposed combining a PCA-LPP-based algorithm with a locality preserving projection (LPP). However, the existing PCA-LPP method is unsuitable for 3D face reconstruction because it focuses on data classification and clustering. In the existing PCA-LPP, the adjacency graph, which primarily shows the connection relationships between data, is composed of the e-or k-nearest neighbor techniques. By contrast, in this study,… More >

Xiaorui Zhang^1,2,3,*, Feng Xu², Wei Sun^3,4, Yan Jiang², Yi Cao⁵

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1695-1709, 2023, DOI:10.32604/csse.2023.031506

Abstract 3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one perspective. When existing methods reconstruct the mesh surface of complex objects, the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework; the 3D topology is easily limited by predefined templates and inflexible, and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology, thus destroying the surface details; the training of the reconstruction network is limited by the large amount of information attached… More >

Taku Itoh¹, Susumu Nakata²

CMES-Computer Modeling in Engineering & Sciences, Vol.107, No.3, pp. 187-199, 2015, DOI:10.3970/cmes.2015.107.187

Abstract To speed up generating a scalar field g(x) based on a piecewise polynomial, a new method for determining field values that are indispensable to generate g(x) has been proposed. In the proposed method, an intermediate for generating g(x) does not required, i.e., the field values can directly be determined from given point data. Numerical experiments show that the computation time for determining the field values by the proposed method is about 10.4–12.7 times less than that of the conventional method. In addition, on the given points, the accuracy of g(x) obtained by using the proposed method is almost the same… More >