# Line integral convolution

The Large Magellanic Cloud (LMC), one of the nearest galaxies to our Milky Way. This image was created with LIC

In scientific visualization, line integral convolution (LIC) is a method to visualize a vector field, such as fluid motion.

## Features

### Convolution

In signal processing this process is known as discrete convolution.[1]

### Integration-based method

It is the integration-based method (technique). More precisely it is based on the discrete line integral on uniform grids. Discrete numerical integration is performed along a line ( more precisely curve). Line here means field line of vector field.

### Global method

Compared to other integration-based techniques that compute field lines of the input vector field, LIC has the advantage that all structural features of the vector field are displayed, without the need to adapt the start and end points of field lines to the specific vector field. In other words, itr shows the topology of the vector field.

In user testing, LIC was found to be particularly good for identifying critical points.[2]

### other

With the availability of high-performance GPU-based implementations, the former disadvantage of limited interactivity is no longer present.

### Texture-based methood

LIC takes texture as an input and gives texture ( output). So it is texture synthesis.

## History

LIC technique was proposed by Brian Cabral and Leith Casey Leedom[3]

## Algorithm

### Informal description

Principle : the pixel values ​​along the field lines are strongly correlated, but orthogonally to them almost uncorrelated. As a result, the field lines stand out visually from the background and become visible.

Intuition:

Intuitively, the flow of a vector field in some domain is visualized by adding a static random pattern of dark and light paint sources. As the flow passes by the sources, each parcel of fluid picks up some of the source color, averaging it with the color it has already acquired in a manner similar to throwing paint in a river. The result is a random striped texture where points along the same streamline tend to have similar color.

### Formal mathematical description

Although the input vector field and the result image are discretized, it pays to look at it from a continuous viewpoint.[5] Let ${\displaystyle \mathbf {v} }$ be the vector field given in some domain ${\displaystyle \Omega }$. Although the input vector field is typically discretized, we regard the field ${\displaystyle \mathbf {v} }$ as defined in every point of ${\displaystyle \Omega }$, i.e. we assume an interpolation. Streamlines, or more generally field lines, are tangent to the vector field in each point. They end either at the boundary of ${\displaystyle \Omega }$ or at critical points where ${\displaystyle \mathbf {v} =\mathbf {0} }$. For the sake of simplicity, in the following critical points and boundaries are ignored. A field line ${\displaystyle {\boldsymbol {\sigma }}}$, parametrized by arc length ${\displaystyle s}$, is defined as ${\displaystyle {\frac {d{\boldsymbol {\sigma }}(s)}{ds}}={\frac {\mathbf {v} ({\boldsymbol {\sigma }}(s))}{|\mathbf {v} ({\boldsymbol {\sigma }}(s))|}}}$. Let ${\displaystyle {\boldsymbol {\sigma }}_{\mathbf {r} }(s)}$ be the field line that passes through the point ${\displaystyle \mathbf {r} }$ for ${\displaystyle s=0}$. Then the image gray value at ${\displaystyle \mathbf {r} }$ is set to

${\displaystyle D(\mathbf {r} )=\int _{-L/2}^{L/2}k(s)N({\boldsymbol {\sigma }}_{\mathbf {r} }(s))ds}$

where ${\displaystyle k(s)}$ is the convolution kernel, ${\displaystyle N(\mathbf {r} )}$ is the noise image, and ${\displaystyle L}$ is the length of field line segment that is followed.

${\displaystyle D(\mathbf {r} )}$ has to be computed for each pixel in the LIC image. If carried out naively, this is quite expensive. First, the field lines have to be computed using a numerical method for solving ordinary differential equations, like a Runge–Kutta method, and then for each pixel the convolution along a field line segment has to be calculated.

The output image will normally be colored in some way. Typically some scalar field in ${\displaystyle \Omega }$ is used, like the vector length, to determine the hue, while the gray-scale LIC image determines the brightness of the color.

Different choices of convolution kernels and random noise produce different textures: for example pink noise produces a cloudy pattern where areas of higher flow stand out as smearing, suitable for weather visualization. Further refinements in the convolution can improve the quality of the image.[6]

### description for programmers

Algorithmically, the technique starts by generating in the domain of the vector field a random gray level image at the desired output resolution. Then, for every pixel in this image, the forward and backward streamline of a fixed arc length is calculated. The value assigned to the current pixel is computed by a convolution of a suitable convolution kernel with the gray levels of all the pixels lying on a segment of this streamline. This creates a gray level LIC image.

#### Problem

Visualise field lines and singularities of a 2D stationary vector field (stream lines of a 2D steady flow)

#### Input

• vector field: 2D stationary vector field
• noise: stationary white noise (scalar field = texture)

#### Output

result (output image) = final LIC image of input vector field

#### constraints

• false singularities

## Versions

### Basic

Basic LIC visualization of a flow field.

Basic LIC images are grayscale images, without color and animation. While such LIC image conveys the orientation of the field vectors, it does not indicate their direction; for stationary fields, this can be remedied by animation. Basic LIC images do not show the length of the vectors (or the strength of the field).

### Color

LIC visualization with color denoting velocity magnitude.

The length of the vectors (or the strength of the field) is usually coded in color; alternatively, animation can be used.[3][5]

### Animated version

Illustration on how to animate. Above: Normal Box Filter (average). Middle: Sinusoidal Filter at ${\displaystyle t}$. Bottom: Sinusoidal Filter at ${\displaystyle t+\delta t}$

LIC images can be animated by using a kernel that changes over time. Samples at a constant time from the streamline would still be used, but instead of averaging all pixels in a streamline with a static kernel, a ripple-like kernel constructed from a periodic function multiplied by a Hann function acting as a window (in order to prevent artifacts) is used. The periodic function is then shifted along the period to create an animation.

### FLIC

Fast Lic ( FLIC)

The computation can be significantly accelerated by re-using parts of already computed field lines, specializing to a box function as convolution kernel ${\displaystyle k(s)}$ and avoiding redundant computations during convolution.[5] The resulting fast LIC method can be generalized to convolution kernels that are arbitrary polynomials.[7]

### OLIC

Oriented Line Integral Convolution (OLIC)

• Fast Rendering of Oriented Line Integral Convolution ( FROLIC)[8][9]

### UFLIC

For time-dependent vector fields ( unsteady flow) a variant (UFLIC = Unsteady Flow LIC), has been designed that maintains the coherence of the flow animation.[10]

### Parallel versions

Since the computation of a LIC image is expensive but inherently parallel, it has also been parallelized[11] and, with availability of GPU-based implementations, it has become interactive on PCs. Also for UFLIC an interactive GPU-based implementation has been presented.[12]

### Multidimensional

Note that domain ${\displaystyle \Omega }$ does not have to be a 2D domain: the method is applicable to higher dimensional domains using multidimensional noise fields. However, the visualization of the higher-dimensional LIC texture is problematic; one way is to use interactive exploration with 2D slices that are manually positioned and rotated. The domain ${\displaystyle \Omega }$ does not have to be flat either; the LIC texture can be computed also for arbitrarily shaped 2D surfaces in 3D space.[13]

## Applications

This technique has been applied to a wide range of problems since it first was published in 1993.

Applications:

• representing vector fields by approximation a field lines of the input vector field
• flow-visualization method for steady ( time independent) flows.[14] Here field lines are called stremlines
• Visual exploration of 2D autonomous dynamical systems[15]
• windy maps[16]
• water directions
• artistic effects to generate fancy images
• pencil drawing[17]
• automatic pencil drawing generation technique using LIC pencil filter[18]
• Automatic generation of hair texture[19]
• creating marbling texture[20]
• Terrain generalization: creating generalized shaded relief[21]

## References

1. ^ Line Integral Convolution for Flow Visualization by Dr. Matthew O. Ward
2. ^ Laidlaw, David H.; Kirby, Robert M.; Davidson, J. Scott; Miller, Timothy S.; da Silva, Marco; Warren, William H.; Tarr, Michael J. (October 21–26, 2001). "Quantitative Comparative Evaluation of 2D Vector Field Visualization Methods". IEEE Visualization 2001, VIS '01. Proceedings. San Diego, CA, USA. pp. 143–150.
3. ^ a b Cabral, Brian; Leedom, Leith Casey (August 2–6, 1993). "Imaging Vector Fields Using Line Integral Convolution". Proceedings of the 20th annual conference on Computer graphics and interactive techniques. SIGGRAPH '93. Anaheim, California. pp. 263–270. CiteSeerX 10.1.1.115.1636. doi:10.1145/166117.166151. ISBN 0-89791-601-8.
4. ^ LIC by Zhanping Liu
5. ^ a b c Stalling, Detlev; Hege, Hans-Christian (August 6–11, 1995). "Fast and Resolution Independent Line Integral Convolution". Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques. SIGGRAPH '95. Los Angeles, California. pp. 249–256. CiteSeerX 10.1.1.45.5526. doi:10.1145/218380.218448. ISBN 0-89791-701-4.
6. ^ Weiskopf, Daniel (2009). "Iterative Twofold Line Integral Convolution for Texture-Based Vector Field Visualization". In Möller, Torsten; Hamann, Bernd; Russell, Robert D. (eds.). Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration. Mathematics and Visualization. Berlin, New York: Springer. pp. 191–211. CiteSeerX 10.1.1.66.3013. doi:10.1007/b106657_10. ISBN 978-3-540-25076-0.
7. ^ Hege, Hans-Christian; Stalling, Detlev (1998), "Fast LIC with piecewise polynomial filter kernels", in Hege, Hans-Christian; Polthier, Konrad (eds.), Mathematical Visualization, Berlin, Heidelberg: Springer-Verlag, pp. 295–314, CiteSeerX 10.1.1.31.504, doi:10.1007/978-3-662-03567-2_22, ISBN 978-3-642-08373-0
8. ^ Fast Oriented Line Integral Convolution for Vector Field Visualization via the Internet by Rainer Wegenkittl and Eduard Gr¨oller
9. ^ Java Exploration Tool for Dynamical Systems by R. Wegenkittl and E. Gröller.
10. ^ Shen, Han-Wei; Kam, David L. (1998). "A New Line Integral Convolution Algorithm for Visualizing Time-Varying Flow Fields" (PDF). IEEE Trans Vis Comput Graph. Los Alamitos: IEEE. 4 (2): 98–108. doi:10.1109/2945.694952. ISSN 1077-2626.
11. ^ Zöckler, Malte; Stalling, Detlev; Hege, Hans-Christian (1997). "Parallel Line Integral Convolution" (PDF). Parallel Computing. Amsterdam: North Holland. 23 (7): 975–989. doi:10.1016/S0167-8191(97)00039-2. ISSN 0167-8191.
12. ^ Ding, Zi'ang; Liu, Zhanping; Yu, Yang; Chen, Wei (2015). "Parallel unsteady flow line integral convolution for high-performance dense visualization". 2015 IEEE Pacific Visualization Symposium, PacificVis 2015. Hangzhou, China. pp. 25–30.
13. ^ Battke, Henrik; Stalling, Detlev; Hege, Hans-Christian (1997). "Fast Line Integral Convolution for Arbitrary Surfaces in 3D". In Hege, Hans-Christian; Polthier, Konrad (eds.). Visualization and Mathematics: Experiments, Simulations, and Environments. Berlin, New York: Springer. pp. 181–195. CiteSeerX 10.1.1.71.7228. doi:10.1007/978-3-642-59195-2_12. ISBN 3-540-61269-6.
14. ^ DAAC: Line Integral Convolution
15. ^ Visual exploration of 2D autonomous dynamical systems Thomas Müller2,1 and Filip Sadlo1 Published 26 February 2015 • © 2015 IOP Publishing Ltd European Journal of Physics, Volume 36, Number 3
16. ^ A real-time map of the wind in the U.S. by Fernanda Viégas and Martin Wattenberg.
17. ^ researchgate publication: Sun, Shuo & Huang, Dongwei. (2022). Efficient Region-Based Pencil Drawing.
18. ^ S. Yamamoto, Xiaoyang Mo and A. Imamiya, "Enhanced LIC pencil filter," Proceedings. International Conference on Computer Graphics, Imaging and Visualization, 2004. CGIV 2004., 2004, pp. 251-256, doi: 10.1109/CGIV.2004.1323994.
19. ^ Xiaoyang Mao, M. Kikukawa, K. Kashio and A. Imamiya, "Automatic generation of hair texture with line integral convolution," 2000 IEEE Conference on Information Visualization. An International Conference on Computer Visualization and Graphics, 2000, pp. 303-308, doi: 10.1109/IV.2000.859772.
20. ^ Xiaoyang Mao, Toshikazu Suzuki, and Atsumi Imamiya. 2003. AtelierM: a physically based interactive system for creating traditional marbling textures. In Proceedings of the 1st international conference on Computer graphics and interactive techniques in Australasia and South East Asia (GRAPHITE '03). Association for Computing Machinery, New York, NY, USA, 79–ff. https://doi.org/10.1145/604471.604489
21. ^ Bernhard Jenny (2021) Terrain generalization with line integral convolution, Cartography and Geographic Information Science, 48:1, 78-92, DOI: 10.1080/15230406.2020.1833762