Boundary-structure-aware transfer functions for volume classification

Lina Yu, Hongfeng Yu

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

2 Citations (Scopus)

Abstract

We present novel transfer functions that advance the classification of volume data by combining the advantages of the existing boundary-based and structure-based methods. We introduce the usage of the standard deviation of ambient occlusion to quantify the variation of both boundary and structure information across voxels, and name our method as boundary-structure-aware transfer functions. Our method gives concrete guidelines to better reveal the interior and exterior structures of features, especially for occluded objects without perfect homogeneous intensities. Furthermore, our method separates these patterns from other materials that may contain similar average intensities, but with different intensity variations. The proposed method extends the expressiveness and the utility of volume rendering in extracting the continuously changed patterns and achieving more robust volume classifications.

Original languageEnglish (US)
Title of host publicationSIGGRAPH Asia 2017 Symposium on Visualization, SA 2017
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450354110
DOIs
StatePublished - Nov 27 2017
EventSIGGRAPH Asia 2017 Symposium on Visualization, SA 2017 - Bangkok, Thailand
Duration: Nov 27 2017Nov 30 2017

Publication series

NameSIGGRAPH Asia 2017 Symposium on Visualization, SA 2017

Other

OtherSIGGRAPH Asia 2017 Symposium on Visualization, SA 2017
CountryThailand
CityBangkok
Period11/27/1711/30/17

Fingerprint

Transfer functions
Volume rendering
Concretes

Keywords

  • Classification
  • Transfer functions
  • Volume rendering

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Software

Cite this

Yu, L., & Yu, H. (2017). Boundary-structure-aware transfer functions for volume classification. In SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017 [3139306] (SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017). Association for Computing Machinery, Inc. https://doi.org/10.1145/3139295.3139306

Boundary-structure-aware transfer functions for volume classification. / Yu, Lina; Yu, Hongfeng.

SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017. Association for Computing Machinery, Inc, 2017. 3139306 (SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017).

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

Yu, L & Yu, H 2017, Boundary-structure-aware transfer functions for volume classification. in SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017., 3139306, SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017, Association for Computing Machinery, Inc, SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017, Bangkok, Thailand, 11/27/17. https://doi.org/10.1145/3139295.3139306
Yu L, Yu H. Boundary-structure-aware transfer functions for volume classification. In SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017. Association for Computing Machinery, Inc. 2017. 3139306. (SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017). https://doi.org/10.1145/3139295.3139306
Yu, Lina ; Yu, Hongfeng. / Boundary-structure-aware transfer functions for volume classification. SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017. Association for Computing Machinery, Inc, 2017. (SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017).
@inproceedings{3df264a30b064208b34b4c65ed269337,
title = "Boundary-structure-aware transfer functions for volume classification",
abstract = "We present novel transfer functions that advance the classification of volume data by combining the advantages of the existing boundary-based and structure-based methods. We introduce the usage of the standard deviation of ambient occlusion to quantify the variation of both boundary and structure information across voxels, and name our method as boundary-structure-aware transfer functions. Our method gives concrete guidelines to better reveal the interior and exterior structures of features, especially for occluded objects without perfect homogeneous intensities. Furthermore, our method separates these patterns from other materials that may contain similar average intensities, but with different intensity variations. The proposed method extends the expressiveness and the utility of volume rendering in extracting the continuously changed patterns and achieving more robust volume classifications.",
keywords = "Classification, Transfer functions, Volume rendering",
author = "Lina Yu and Hongfeng Yu",
year = "2017",
month = "11",
day = "27",
doi = "10.1145/3139295.3139306",
language = "English (US)",
series = "SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017",
publisher = "Association for Computing Machinery, Inc",
booktitle = "SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017",

}

TY - GEN

T1 - Boundary-structure-aware transfer functions for volume classification

AU - Yu, Lina

AU - Yu, Hongfeng

PY - 2017/11/27

Y1 - 2017/11/27

N2 - We present novel transfer functions that advance the classification of volume data by combining the advantages of the existing boundary-based and structure-based methods. We introduce the usage of the standard deviation of ambient occlusion to quantify the variation of both boundary and structure information across voxels, and name our method as boundary-structure-aware transfer functions. Our method gives concrete guidelines to better reveal the interior and exterior structures of features, especially for occluded objects without perfect homogeneous intensities. Furthermore, our method separates these patterns from other materials that may contain similar average intensities, but with different intensity variations. The proposed method extends the expressiveness and the utility of volume rendering in extracting the continuously changed patterns and achieving more robust volume classifications.

AB - We present novel transfer functions that advance the classification of volume data by combining the advantages of the existing boundary-based and structure-based methods. We introduce the usage of the standard deviation of ambient occlusion to quantify the variation of both boundary and structure information across voxels, and name our method as boundary-structure-aware transfer functions. Our method gives concrete guidelines to better reveal the interior and exterior structures of features, especially for occluded objects without perfect homogeneous intensities. Furthermore, our method separates these patterns from other materials that may contain similar average intensities, but with different intensity variations. The proposed method extends the expressiveness and the utility of volume rendering in extracting the continuously changed patterns and achieving more robust volume classifications.

KW - Classification

KW - Transfer functions

KW - Volume rendering

UR - http://www.scopus.com/inward/record.url?scp=85040035042&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85040035042&partnerID=8YFLogxK

U2 - 10.1145/3139295.3139306

DO - 10.1145/3139295.3139306

M3 - Conference contribution

AN - SCOPUS:85040035042

T3 - SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017

BT - SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017

PB - Association for Computing Machinery, Inc

ER -