TY - JOUR
T1 - GLoG
T2 - Laplacian of Gaussian for Spatial Pattern Detection in Spatio-Temporal Data
AU - Nonato, Luis Gustavo
AU - Do Carmo, Fabiano Petronetto
AU - Silva, Claudio T.
N1 - Funding Information:
This work was supported in part by: 302643/2013-3 CNPq-Brazil and 2016/04391-2, 2014/12236-1 and 2013/07375-0 São Paulo Research Foundation (FAPESP) - Brazil; the Moore-Sloan Data Science Environment at NYU; NASA; NSF awards CNS-1229185, CCF-1533564, CNS-1544753, CNS-1730396, CNS-1828576, CNS-1626098; and the NVIDIA NVAIL at NYU. The views expressed are those of the authors and do not reflect the official policy or position of the São Paulo Research Foundation. C. T. Silva is partially supported by the DARPA D3M program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA.
Publisher Copyright:
© 1995-2012 IEEE.
PY - 2021/8/1
Y1 - 2021/8/1
N2 - Boundary detection has long been a fundamental tool for image processing and computer vision, supporting the analysis of static and time-varying data. In this work, we built upon the theory of Graph Signal Processing to propose a novel boundary detection filter in the context of graphs, having as main application scenario the visual analysis of spatio-temporal data. More specifically, we propose the equivalent for graphs of the so-called Laplacian of Gaussian edge detection filter, which is widely used in image processing. The proposed filter is able to reveal interesting spatial patterns while still enabling the definition of entropy of time slices. The entropy reveals the degree of randomness of a time slice, helping users to identify expected and unexpected phenomena over time. The effectiveness of our approach appears in applications involving synthetic and real data sets, which show that the proposed methodology is able to uncover interesting spatial and temporal phenomena. The provided examples and case studies make clear the usefulness of our approach as a mechanism to support visual analytic tasks involving spatio-temporal data.
AB - Boundary detection has long been a fundamental tool for image processing and computer vision, supporting the analysis of static and time-varying data. In this work, we built upon the theory of Graph Signal Processing to propose a novel boundary detection filter in the context of graphs, having as main application scenario the visual analysis of spatio-temporal data. More specifically, we propose the equivalent for graphs of the so-called Laplacian of Gaussian edge detection filter, which is widely used in image processing. The proposed filter is able to reveal interesting spatial patterns while still enabling the definition of entropy of time slices. The entropy reveals the degree of randomness of a time slice, helping users to identify expected and unexpected phenomena over time. The effectiveness of our approach appears in applications involving synthetic and real data sets, which show that the proposed methodology is able to uncover interesting spatial and temporal phenomena. The provided examples and case studies make clear the usefulness of our approach as a mechanism to support visual analytic tasks involving spatio-temporal data.
KW - Data filtering
KW - data transformation
KW - feature detection
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U2 - 10.1109/TVCG.2020.2978847
DO - 10.1109/TVCG.2020.2978847
M3 - Article
C2 - 32149640
AN - SCOPUS:85111789216
SN - 1077-2626
VL - 27
SP - 3481
EP - 3492
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 8
M1 - 9026910
ER -