scholarly journals Development of Interactive 3D Volume Visualization Techniques Using Contour Trees

2011 ◽  
Vol 16 (11) ◽  
pp. 67-76
Author(s):  
Bong-Soo Sohn
Keyword(s):  
Volume Visualization ◽  
Contour Trees ◽  
Interactive 3D ◽  
3D Volume ◽  
Visualization Techniques

Visualizing the Time Motion of Planar Mechanisms With Volume Methods

2005 ◽  
Author(s):  
Mike Bailley
Keyword(s):  
Volume Visualization ◽  
Spatial Dimension ◽  
Three Dimensions ◽  
Time Dimension ◽  
Modeling And Analysis ◽  
Planar Mechanism ◽  
Time Motion ◽  
Novel Approach ◽  
3D Volume ◽  
Visualization Techniques

Two dimensional, or planar, mechanism design is a mainstay of Mechanical Engineering modeling and analysis. An important part of the design process is the visualizing of the motion of the mechanism. This paper describes a novel approach to visualizing the time motion of a planar mechanism — turning the time dimension into a spatial dimension. All three dimensions (x,y,time) are then treated as a 3D volume. From there, we use interactive volume visualization techniques, including slicing and thresholding. As is seen, this method is able to produce new insights into planar mechanism motion, particularly when more than one mechanism is working cooperatively.

scholarly journals Interactive 3D volume rendering in biomedical publications

Micron ◽  
2010 ◽  
Vol 41 (7) ◽  
pp. 886.e1-886.e17 ◽  
Author(s):  
Bernhard Ruthensteiner ◽  
Natalie Baeumler ◽  
David G. Barnes
Keyword(s):  
Volume Rendering ◽  
Interactive 3D ◽  
Biomedical Publications ◽  
3D Volume ◽  
3D Volume Rendering

scholarly journals A Clustering-Based Automatic Transfer Function Design for Volume Visualization

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Tianjin Zhang ◽  
Zongrui Yi ◽  
Jinta Zheng ◽  
Dong C. Liu ◽  
Wai-Mai Pang ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Information ◽  
Transfer Functions ◽  
Clustering Algorithms ◽  
Volume Visualization ◽  
Automatic Generation ◽  
Volume Data ◽  
3D Volume ◽  
Ap Clustering ◽  
Different Tissues

The two-dimensional transfer functions (TFs) designed based on intensity-gradient magnitude (IGM) histogram are effective tools for the visualization and exploration of 3D volume data. However, traditional design methods usually depend on multiple times of trial-and-error. We propose a novel method for the automatic generation of transfer functions by performing the affinity propagation (AP) clustering algorithm on the IGM histogram. Compared with previous clustering algorithms that were employed in volume visualization, the AP clustering algorithm has much faster convergence speed and can achieve more accurate clustering results. In order to obtain meaningful clustering results, we introduce two similarity measurements: IGM similarity and spatial similarity. These two similarity measurements can effectively bring the voxels of the same tissue together and differentiate the voxels of different tissues so that the generated TFs can assign different optical properties to different tissues. Before performing the clustering algorithm on the IGM histogram, we propose to remove noisy voxels based on the spatial information of voxels. Our method does not require users to input the number of clusters, and the classification and visualization process is automatic and efficient. Experiments on various datasets demonstrate the effectiveness of the proposed method.

3D Volume Visualization and Screen-based Interaction with Dynamic Ray Casting on Autostereoscopic Display

2021 ◽  
Author(s):  
Ruiyang Li ◽  
Tianqi Huang ◽  
Hanying Liang ◽  
Boxuan Han ◽  
Xinran Zhang ◽  
...  
Keyword(s):  
Volume Visualization ◽  
Ray Casting ◽  
Autostereoscopic Display ◽  
3D Volume
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