Abstract
Texture adaptation is a challenging issue in tex-ture-based feature visualization. In order to visualize as more information as we can, this paper presents a texture adaptation technique for fuzzy feature visualization of 3D vector field, taking into account information quantity carried by vector field and texture based on extended information entropy. Two definitions of information measurement for 3D vector field and noise texture, MIE and RNIE, are proposed to quantitatively represent the information carried by them. A noise generation algorithm based on three principles derived from minimal differentia of MIE and RNIE is designed to obtain an approximately optimal distribution of noise fragments which shows more details than those used before. A discussion of results is included to demonstrate our algorithm which leads to a more reasonable visualization results based on fuzzy feature measurement and information quantity.
Highlights
Vector field visualization plays an important role in scientific research and is widely used to analyze data originating from numerical simulations or measurements such as those of computational fluid dynamics (CFD), climate modeling, and electromagnetism
This paper proposes a rational measurement for vector field and noise texture, and presents a noise generation algorithm based on minimal differentia between them
In order to make the differentia of Membership Information Entropy (MIE) and Rendered Noise Information Entropy (RNIE) nearly reaches its minimum, noise fragment percentage pi should be as closer to membership degree percentage pmi as (a) possible
Summary
Vector field visualization plays an important role in scientific research and is widely used to analyze data originating from numerical simulations or measurements such as those of computational fluid dynamics (CFD), climate modeling, and electromagnetism. These data is too confused to analyze directly and should be processed in some efficient approaches such as visualization methods. Two extended information entropy, MIE and RNIE are proposed to measure the information carried by this fuzzy measurement field and the noise texture field generated from it. A texture adaptation algorithm is added in order to obtain a visualization result with maximum information defined by the above concepts
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