Fast LIC image generation based on significance map

Li Chen, Issei Fujishiro, Qunshen G. Peng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Although texture-based methods provide a very promising way to visualize 3D vector fields, they are very time-consuming. In this paper, we introduce the notion of “significance map”, and describe how significance values are derived from the intrinsic properties of a vector field. Based on the significance map, we propose techniques to accelerate the generation of a line integral convolution (LIC) texture image, to highlight important structures in a vector field, and to generate an LIC texture image with different granularities. Also, we describe how to implement our method in a parallel environment. Experimental results illustrate the feasibility of our method.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages537-546
Number of pages10
Volume1940
ISBN (Print)9783540411284
Publication statusPublished - 2000
Externally publishedYes
Event3rd International Symposium on High Performance Computing, ISHPC 2000 - Tokyo, Japan
Duration: 2000 Oct 162000 Oct 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1940
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Symposium on High Performance Computing, ISHPC 2000
CountryJapan
CityTokyo
Period00/10/1600/10/18

Fingerprint

Curvilinear integral
Convolution
Texture
Vector Field
Textures
Granularity
Accelerate
Experimental Results

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Chen, L., Fujishiro, I., & Peng, Q. G. (2000). Fast LIC image generation based on significance map. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1940, pp. 537-546). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1940). Springer Verlag.

Fast LIC image generation based on significance map. / Chen, Li; Fujishiro, Issei; Peng, Qunshen G.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1940 Springer Verlag, 2000. p. 537-546 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1940).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chen, L, Fujishiro, I & Peng, QG 2000, Fast LIC image generation based on significance map. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 1940, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1940, Springer Verlag, pp. 537-546, 3rd International Symposium on High Performance Computing, ISHPC 2000, Tokyo, Japan, 00/10/16.
Chen L, Fujishiro I, Peng QG. Fast LIC image generation based on significance map. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1940. Springer Verlag. 2000. p. 537-546. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Chen, Li ; Fujishiro, Issei ; Peng, Qunshen G. / Fast LIC image generation based on significance map. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1940 Springer Verlag, 2000. pp. 537-546 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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