.png){kind=icon}
.png){kind=icon}
Return
Feature-based adaptive texture visualization for vector field
.png){kind=icon}
PRE
AI
DOI：10.1007/s11432-011-4505-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
An adaptive sparse texture rendering method is proposed to solve for occlusion effects when visualizing 3D flows, building on an extensible fuzzy feature extraction approach. First, the flow feature is described by fuzzy theory and rules for some typical features are obtained. The significance value for each voxel is then calculated by a clustering method under the minimum square-sum rule. An adaptive Gaussian noise field is obtained from the significance field by a noise generation process, and is used as the input for the LIC convolution process. We also present two cool/warm-illumination-based approaches to overcome the shortcomings of texture-based visualization methods, which are usually unable to represent the flow direction. The experiments show that our method can effectively extract the typical flow feature region and can be extended to other flow features easily, and the adaptive technique used lessens the occlusion effects significantly. Furthermore, the main disadvantage of the texture-based method, that is, the direction representation problem, can also be solved by the proposed cool/warm illumination methods.
Keywords:
vector field
feature visualization
adaptive processing
fuzzy theory
feature structure
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
4.9K
Citations:
8.9K