A Novel 3D Model Segmentation Approach

Author(s):  
Y. Wang ◽  
R. Liu ◽  
S. Endo ◽  
Y. Uehara
2021 ◽  
Vol 29 ◽  
pp. 133-140
Author(s):  
Bin Liu ◽  
Shujun Liu ◽  
Guanning Shang ◽  
Yanjie Chen ◽  
Qifeng Wang ◽  
...  

BACKGROUND: There is a great demand for the extraction of organ models from three-dimensional (3D) medical images in clinical medicine diagnosis and treatment. OBJECTIVE: We aimed to aid doctors in seeing the real shape of human organs more clearly and vividly. METHODS: The method uses the minimum eigenvectors of Laplacian matrix to automatically calculate a group of basic matting components that can properly define the volume image. These matting components can then be used to build foreground images with the help of a few user marks. RESULTS: We propose a direct 3D model segmentation method for volume images. This is a process of extracting foreground objects from volume images and estimating the opacity of the voxels covered by the objects. CONCLUSIONS: The results of segmentation experiments on different parts of human body prove the applicability of this method.


2014 ◽  
Vol 1049-1050 ◽  
pp. 1417-1420
Author(s):  
Hui Jia ◽  
Guo Hua Geng ◽  
Jian Gang Zhang

3D model segmentation is a new research focus in the field of computer graphics. The segmentation algorithm of this paper is consistent segmentation which is about a group of 3D model with shape similarity. A volume-based shape-function called the shape diameter function (SDF) is used to on behalf of the characteristics of the model. Gaussian mixture model (GMM) is fitting k Gaussians to the SDF values, and EM algorithm is used to segment 3D models consistently. The experimental results show that this algorithm can effectively segment the 3D models consistently.


2008 ◽  
Vol E91-D (4) ◽  
pp. 1149-1158 ◽  
Author(s):  
B. ZHENG ◽  
J. TAKAMATSU ◽  
K. IKEUCHI

Author(s):  
Joseph C. Tsai ◽  
Shih Ming Chang ◽  
Shwu Huey Yen ◽  
Timothy K. Shih ◽  
Kuan Ching Li

2007 ◽  
Vol 30 (4) ◽  
pp. 675-687 ◽  
Author(s):  
Shyi‐Chyi Cheng ◽  
Chen‐Tsung Kuo ◽  
Wei‐Ming Lai

2014 ◽  
Vol 45 (6) ◽  
pp. 893-907 ◽  
Author(s):  
Changan Yan ◽  
Wanchang Zhang

Although models are one of the most powerful tools for watershed management, their effectiveness is limited by prediction uncertainties resulting from not only model input data but also spatial discretization. In this paper, Hydrological Simulation Program – Fortran (HSPF) models were constructed for the Linyi watershed according to three segmentation approaches including model segments based on differences in: (1) sub-watershed, (2) meteorological station, and (3) physical characteristics. Then the static sensitivity method and dynamic sensitivity method were employed to evaluate the effect of the segmentation approach on model performance and parameters of HSPF. The main conclusions were: (1) modeling with 12 segments had the best simulation efficiency and the corresponding estimated parameters had a certain representation within the Linyi watershed; (2) HSPF model performance was significantly affected by the segmentation approach, especially by the model segmentation construction process which considering a meteorological station or not; (3) parameters INTFW (interflow inflow parameter), lower zone nominal storage, and upper zone nominal storage (UZSN) were most affected by the model segmentation approach, while parameter AGWRC (groundwater recession coefficient) changed indistinctly; (4) parameters UZSN and INTFW had the same variation tendency whenever the segmentation approach changed.


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