Enhanced line integral convolution with flow feature detection

Arthur Okada; David L. Kao

doi:10.1117/12.270314

9 April 1997 Enhanced line integral convolution with flow feature detection

Arthur Okada, David L. Kao

Proceedings Volume 3017, Visual Data Exploration and Analysis IV; (1997) https://doi.org/10.1117/12.270314
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

Abstract

The line integral convolution (LIC) method, which blurs white noise textures along a vector field, is an effective way to visualize overall flow patterns in a 2D domain. The method produces a flow texture image based on the input velocity field defined in the domain. Because of the nature of the algorithm, the texture image tends to be blurry. This sometimes makes it difficult to identify boundaries where flow separation and re-attachments occur. We present techniques to enhance LIC texture images and use colored texture images to highlight flow separation and re- attachment boundaries. Our techniques have been applied to several flow fields defined in 3D curvilinear multi-block grids and scientists have found the results to be very useful.

Citation Download Citation

Arthur Okada and David L. Kao "Enhanced line integral convolution with flow feature detection", Proc. SPIE 3017, Visual Data Exploration and Analysis IV, (9 April 1997); https://doi.org/10.1117/12.270314

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