A unified framework for exploring time-varying volumetric data based on block correspondence

Lu, Kecheng; Wang, Chaoli; Wu, Keqin; Gong, Minglun; Wang, Yunhai

doi:10.1016/j.visinf.2019.10.001

i

A unified framework for exploring time-varying volumetric data based on block correspondence

2019
By Lu, Kecheng ; Wang, Chaoli ; Wu, Keqin ; ...

Details

Journal Title:

Visual Informatics
Personal Author:

Lu, Kecheng ; Wang, Chaoli ; Wu, Keqin ; Gong, Minglun ; Wang, Yunhai
NOAA Program & Office:

NOAA General Documents
Description:

Effective exploration of spatiotemporal volumetric data sets remains a key challenge in scientific visualization. Although great advances have been made over the years, existing solutions typically focus on only one or two aspects of data analysis and visualization. A streamlined workflow for analyzing time-varying data in a comprehensive and unified manner is still missing. Towards this goal, we present a novel approach for time-varying data visualization that encompasses keyframe identification, feature extraction and tracking under a single, unified framework. At the heart of our approach lies in the GPU-accelerated BlockMatch method, a dense block correspondence technique that extends the PatchMatch method from 2D pixels to 3D voxels. Based on the results of dense correspondence, we are able to identify keyframes from the time sequence using k-medoids clustering along with a bidirectional similarity measure. Furthermore, in conjunction with the graph cut algorithm, this framework enables us to perform fine-grained feature extraction and tracking. We tested our approach using several time-varying data sets to demonstrate its effectiveness and utility.
Keywords:

Computer Graphics And Computer-Aided Design Human-Computer Interaction Software Computer Graphics And Computer-Aided Design Computer Graphics And Computer-Aided Design
Source:

Visual Informatics, 3(4), 157-165
DOI:

https://doi.org/10.1016/j.visinf.2019.10.001
ISSN:

2468-502X
Format:

PDF
Publisher:

Elsevier BV
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by-nc-nd/4.0/
Rights Information:

CC BY-NC-ND
Compliance:

Library
Main Document Checksum:

urn:sha-512:e7d0323f74f84094a92936e9fd8ad6bf95a976869a1fb6dc6bcac4087bb2387493e723045d5eabfec079b0617a4695af3f69c8e54223f6ae8b0db0c00fcfdc01
Download URL:

https://repository.library.noaa.gov/view/noaa/61975/noaa_61975_DS1.pdf
File Type:

[PDF - 1.47 MB ]

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