Fast and resolution independent line integral convolution

Abstract

Line Integral Convolution (LIC) is a powerful technique for generating striking images and animations from vector data. Introduced in 1993, the method has rapidly found many application areas, ranging from computer arts to scientific visualization. Based upon locally filtering an input texture along a curved stream line segment in a vector field, it is able to depict directional information at high spatial resolutions.We present a new method for computing LIC images. It employs simple box filter kernels only and minimizes the total number of stream lines to be computed. Thereby it reduces computational costs by an order of magnitude compared to the original algorithm. Our method utilizes fast, error-controlled numerical integrators. Decoupling the characteristic lengths in vector field grid, input texture and output image, it allows computation of filtered images at arbitrary resolution. This feature is of significance in computer animation as well as in scientific visualization, where it can be used to explore vector data by smoothly enlarging structure of details.We also present methods for improved texture animation, again employing box filter kernels only. To obtain an optimal motion effect, spatial decay of correlation between intensities of distant pixels in the output image has to be controlled. This is achieved by blending different phase-shifted box filter animations and by adaptively rescaling the contrast of the output frames.

Applications of Texture-Based Flow Visualization

“…where T stands for an input noise texture, k denotes the filter kernel, s is an arc length used to parameterize the streamline curve, and L represents the filter kernel length (Laramee et al, 2004a;Stalling and Hege, 1995). The result of this convolution is streaks in the texture in the direction of the local flow field.…”

“…1 The visualization of swirl motion at the inner boundary surface of a combustion chamber from an automotive engine cylinder: (left) geometric flow visualization using streamlines, and (right) texture-based flow visualization, both being used in combination with velocity magnitude color-map. (Cabral and Leedom, 1993;Stalling and Hege, 1995). Texture-based visualizations avoid the seeding problem associated with geometric techniques.…”

Comparing LIC and spot noise

“…The timing results presented in [11] indicate that 4.6 seconds are needed for the generation a similar LIC texture on slightly slower hardware using the acceleration techniques described in the paper. This would suggest that LIC is slightly faster than spot noise.…”

“…Several ways have been proposed to speed up the algorithms. In the algorithm for LIC proposed by Stalling and Hege [11] the algorithm consists of two steps. In the first step stream lines are calculated and in the second step the convolution is carried out by successive processing of pixels along streamlines where results are reused.…”

Interactive Poster: Illustrating Different Convection Velocities of Turbulent Flow

“…Textures, such as line integral convolution (LIC), have traditionally been used to visualize vector fields for the purpose of analyzing the * e-mail: urness@cs.umn.edu form and behavior of flow and to infer the underlying behavior of experimentally-generated flow fields [1,3]. The use of textures allows for a consistent and highly-detailed representation of a vector field allowing an observer to both analyze and better understand the dynamics of fluid flow.…”