Although texture-based methods provide a very promising way to visualize 3D vector fields, they are very time-consuming. In this paper, we introduce the notion of “significance map”, and describe how significance values are derived from the intrinsic properties of a vector field. Based on the significance map, we propose techniques to accelerate the generation of a line integral convolution (LIC) texture image, to highlight important structures in a vector field, and to generate an LIC texture image with different granularities. Also, we describe how to implement our method in a parallel environment. Experimental results illustrate the feasibility of our method.
CITATION STYLE
Chen, L., Fujishiro, I., & Peng, Q. G. (2000). Fast LIC image generation based on significance map. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1940, pp. 537–546). Springer Verlag. https://doi.org/10.1007/3-540-39999-2_52
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