Last edited by JoJozuru
Sunday, August 9, 2020 | History

3 edition of Enhanced line integral convolution with flow feature detection found in the catalog.

Enhanced line integral convolution with flow feature detection

Enhanced line integral convolution with flow feature detection

NAS technical report NAS-96-007

  • 241 Want to read
  • 1 Currently reading

Published by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, DC, Springfield, Va .
Written in English

    Subjects:
  • Flow distribution.,
  • Flow visualization.,
  • Image enhancement.,
  • Algorithms.,
  • Separated flow.,
  • Reattached flow.

  • Edition Notes

    StatementArthur Okada, David Lane.
    SeriesNASA contractor report -- NASA CR-203362.
    ContributionsLane, David., United States. National Aeronautics and Space Administration.
    The Physical Object
    FormatMicroform
    Pagination1 v.
    ID Numbers
    Open LibraryOL15543353M

      24 Responses to “Line-integral convolution code” dave says: April 11th, at am. Great work for using IDL with new visualization methods! But I can’t download zip file (It’s may be a wrong link!) Michael Galloy says: April 11th, at am. It’s fixed now. Thanks for letting me know! Line Integral Convolution for Flow Visualization Dr. Matthew O. Ward Computer Science Department WPI. Introduction. The problem with most visualization techniques for flow fields is that the density of information being shown is low - the glyphs or structures used to depict the magnitude and direction of flow (as well as other features) precludes the display of dense information.

      Line Integral Convolution for Flow Visualization Han-Wei Shen. This feature is not available right now. Please try again later. Line Integral Convolution Method - Duration.   Abstract: We propose a largely output-sensitive visualization method for 3D line integral convolution (LIC) whose rendering speed is mainly independent of the data set size and mostly governed by the complexity of the output on the image plane. Our approach of view-dependent visualization tightly links the LIC generation with the volume rendering of the LIC result in order to .

    The authors of presented the Enhanced Line Integral Convolution (ELIC) method to increase the texture contrast, in which white noise was convolved twice for the purpose of improving the contrast. At the same time, the time consumption was inevitably increased. K. Enhanced line integral convolution with flow feature detection. In Proceedings. (Line Integral Convolution)that convolves an input noise texture using a low-pass filter along pixel-centered symmetrically bi-directional streamlines to exploit spatial correlation in the flow direction. LIC synthesizes an image that provides a global dense representation of the.


Share this book
You might also like
Essential Spanish grammar.

Essential Spanish grammar.

The death of a marriage law

The death of a marriage law

ORAVAX, INC.

ORAVAX, INC.

ECONOMICS, REARMAMENT AND FOREIGN POLICY : THE UNITED KINGDOM BEFORE 1939 - A PRELIMINARY STUDY

ECONOMICS, REARMAMENT AND FOREIGN POLICY : THE UNITED KINGDOM BEFORE 1939 - A PRELIMINARY STUDY

Citrus mites

Citrus mites

Xing Yi Quan Xue

Xing Yi Quan Xue

Missions Now

Missions Now

Poetical works (of) Giles and Phineas Fletcher

Poetical works (of) Giles and Phineas Fletcher

Europe without borders

Europe without borders

Collectible Teapot Calendar 2002

Collectible Teapot Calendar 2002

Vindication of the Catholic doctrine concerning the use and veneration of images, the honor and invication [sic] of saints and the keeping & honoring their relics

Vindication of the Catholic doctrine concerning the use and veneration of images, the honor and invication [sic] of saints and the keeping & honoring their relics

Transformations, stone figures from Mezcala/chontal

Transformations, stone figures from Mezcala/chontal

Enhanced line integral convolution with flow feature detection Download PDF EPUB FB2

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 by: Get this from a library. Enhanced line integral convolution with flow feature detection: NAS technical report NAS [Arthur Okada; David Lane; United States. National Aeronautics and. 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 [Cabral & Leedom ‘93].

The method produces a flow texture image based on the input velocity field defined in the domain. Enhanced line integral convolution with flow feature detection Okada, Arthur; Kao, David L.

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.

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 [Cabral & Leedom `93]. 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. 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 [Cabral & Leedom `93].

The method produces a flow texture image based on the input velocity field defined in the domain. Title: Enhanced Line Integral Convolution with Flow Feature Detection Created Date: Z.

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. A. Okada and D. Lane. Enhanced line integral convolution with flow feature detection.

In Proc. IS&T/SPIE Electronic Imagingpages –, Google Scholar. Lisa K. Forsell. Visualizing flow over curvilinear grid surfaces using line integral convolution. InProc. of Visualization ’94, pages – IEEE Press, Los Alamitos, CA, oct CrossRef Google Scholar.

In scientific visualization, line integral convolution (LIC) is a technique to visualize a vector field, like a fluid motion, such as the wind movement in a has been proposed by Brian Cabral and Leith Leedom.

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. The line integral convolution (LIC) technique, a texture synthesis technique, has served as a useful method for visualizing vector data.

However, a number of shortcomings have been identified in the LIC technique, not the least of which are that the technique is computationally expensive and adopts a more or less brute force approach.

Enhanced Line Integral Convolution with Flow Feature Detection Arthur Okada I David Lane 2 NAS Technical Report NAS, June NASA Ames Research Center M/S T27A-2 Moffett Field, CA 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 [Cabral & Leedom '93].

The variations of the Line Integral Convolution method pre-sented so far do not encode the orientation of a flow within a still image. In section 2 Oriented Line Integral Convolution (OLIC) [16] is described, which overcomes this disadvantage. Section 3 discusses a new technique for Fast Rendering of OLIC images (FROLIC).

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 [Cabral & Leedom '93]. The method produces a flow texture image based on the input velocity field defined in the domain. Line Integral Convolution (LIC) is a common approach for the visualisation of 2D vector fields.

It is well suited for visualizing the direction of a flow field, but it gives no information about. Enhanced Line Integral Convolution with Flow Feature Detection () by Arthur Okada and David Lane Unsteady Flow Analysis Toolkit (UFAT) () New Software Tool Improves Interactive Analysis of CFD Numerical Flows ().

The paper presents an algorithm, called UFLIC (Unsteady Flow LIC), to visualize vector data in unsteady flow fields. Our algorithm extends a texture synthesis technique, called Line Integral Convolution (LIC), by devising a new convolution algorithm that uses a time-accurate value scattering scheme to model the texture advection.

Enhanced line integral convolution with flow feature detection Proceedings of SPIE (April 09 ) X window image extension standard what it means to Proceedings of SPIE (June 30 ) Subscribe to Digital Library.

Receive Erratum Email Alert. For the input photograph of Fig. 1(a), the edge flow field is visualized with line integral convolution before (a) and after (b) smoothing. The anisotropy measurement in (c) ranges from 0 to 1, where 1 indicates strongly oriented and 0 indicates an isotropic pattern.

Line Integral Convolution. Line Integral Convolution using openGL shaders in GLSL. Compile It. Depends on openGL headers. On debian based systems: sudo apt-get install mesa-common-dev sudo apt-get install libglu1-mesa-dev sudo apt-get install freeglut3-dev make Run It./LIC.

Usage./LIC [vertex shader] [fragment shader] [lic number of steps].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 [Cabral & Leedom `93]. And the line integral functions in CFD-Post work on all streamlines together, not each streamline separately as the LIC approach requires.

So this all looks possible, but will require some thinking and development.