<title>Enhanced line integral convolution with flow feature detection</title>
Author(s) -
Arthur Okada,
David L. Kao
Publication year - 1997
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.270314
Subject(s) - computer science , convolution (computer science) , computer vision , flow (mathematics) , artificial intelligence , vector field , line (geometry) , texture (cosmology) , feature (linguistics) , curvilinear coordinates , block (permutation group theory) , image texture , image (mathematics) , pattern recognition (psychology) , image processing , mathematics , geometry , linguistics , philosophy , artificial neural network
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.
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