TY - GEN
T1 - Boundary-structure-aware transfer functions for volume classification
AU - Yu, Lina
AU - Yu, Hongfeng
N1 - Funding Information:
This research has been sponsored by the National Science Foundation through grants IIS-1423487 and ICER-1541043. The combustion datasets were provided by Dr. Jacqueline H. Chen at Sandia National Laboratories.
Publisher Copyright:
© 2017 Copyright held by the owner/author(s).
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
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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
T2 - SIGGRAPH Asia 2017 Symposium on Visualization, SA 2017
Y2 - 27 November 2017 through 30 November 2017
ER -