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

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Title: A unified framework for exploring time-varying volumetric data based on block correspondence
Authors: Kecheng Lu, Chaoli Wang, Keqin Wu, Minglun Gong, Yunhai Wang
Source: Visual Informatics, Vol 3, Iss 4, Pp 157-165 (2019)
Publisher Information: Elsevier, 2019.
Publication Year: 2019
Collection: LCC:Information technology
Subject Terms: Information technology, T58.5-58.64
More Details: 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: Time-varying data visualization, Block correspondence, Feature extraction and tracking
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2468-502X
Relation: http://www.sciencedirect.com/science/article/pii/S2468502X19300464; https://doaj.org/toc/2468-502X
DOI: 10.1016/j.visinf.2019.10.001
Access URL: https://doaj.org/article/53933c09e69d4c4188748914c411dae3
Accession Number: edsdoj.53933c09e69d4c4188748914c411dae3
Database: Directory of Open Access Journals
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  Data: Visual Informatics, Vol 3, Iss 4, Pp 157-165 (2019)
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  Data: 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: Time-varying data visualization, Block correspondence, Feature extraction and tracking
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