Bibliographic Details
| 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 |
