[478] Aldrich, G.A., Lukasczyk, J., Hyman, J.D., Srinivasan, G.,
Viswanathan, H.S., Garth, C., Leitte, H., Ahrens, J.P. and Hamann, B. (2020),
A query-based framework for searching, sorting and exploring data ensembles (pdf),
IEEE Computing in Science and Engineering 22(2), pp. 64-76.
[479] Banesh, D., Lo, L.-T., Kilian, P., Guo, F. and Hamann, B. (2020),
Topological analysis of magnetic reconnection in kinetic plasma simulations (pdf),
short paper, in: Bertini, E., Bujack, R., Collins, C., Dou, W.,
Lex, A. and Ropinski, T., eds., Proceedings of IEEE Scientific
Visualization 2020 (SciVis 2020), Short Papers,
IEEE Xplore Digital Library, IEEE Press, Piscataway, New Jersey, pp. 6-10.
[480] Linares, O.A.C., Hamann, B. and Neto, J.B. (2020),
Segmenting cellular retinal images by optimizing super-pixels,
multi-level modularity, and cell boundary representation (pdf),
IEEE Transactions on Image Processing 29(1), pp. 809-818.
[481] Linares, O.A.C., Vargas, A.R.S., Faical, B.S., Hamann, B.,
Fabro, A.T. and Traina, A.J.M. (2020),
Efficient segmentation of cell nuclei in histopathological images (pdf),
in: Garcia Seco de Herrera, A. and Rodriguez, A., eds.,
Proceedings of 33rd IEEE International Symposium on Computer-based Medical Systems
(CBMS 2020), IEEE Xplore Digital Library, IEEE Press, Piscataway, New Jersey, pp. 47-52.
[482] Murugesan, S., Bouchard, K.E., Brown, J., Kiran, M., Lurie, D.,
Hamann, B. and Weber, G.H. (2020),
State-based network similarity visualization (pdf),
Information Visualization 19(2), SAGE Publications Ltd., pp. 96-113.
[483] Pulido, J., Zheng, C., Thorman, P. and Hamann, B. (2020),
SnowPac: A multi-scale cubic B-spline wavelet compressor for astronomical images (pdf),
Monthly Notices of the Royal Astronomical Society (MNRAS) 493(2),
John Wiley & Sons, pp. 2455-2555.
[484] Ruediger, P., Claus, F., Hamann, B., Hagen, H. and Leitte, H. (2020),
Combining visual analytics and machine learning for reverse
engineering in assembly quality control (pdf),
in: Wischgoll, T., Kao, D.L. and Chiang, Y.-J., eds.,
Journal of Imaging Science and Technology 64(6), special issue
(Electronic Imaging 2021 - Visualization and Data Analysis 2021),
Society for Imaging Science and Technology, pp. 060405-1--060405-13.
[485] Vargas, A.R.S., Hamann, B. and Ferreira de Oliveira, M.C. (2020),
TV-MV analytics: A visual analytics framework to explore time-varying multivariate
data (pdf), Information Visualization 19(1), SAGE Publications Ltd., pp. 3-23.
[486] Vargas, A.R.S., Rollmann, K., Almeida, F., Davolio, A., Hamann, B.,
Schiozer, D.J. and Rocha, A. (2020),
A synthetic case study of
measuring the misfit between 4D seismic data and numerical reservoir
simulation models through the momenta tree (pdf),
Computers and Geosciences 145, article 104617, Elsevier, 15 pages.
[487] Vargas, A.R.S., Rollmann, K., Almeida, F., Davolio, A.,
Hamann, B., Schiozer, D.J. and Rocha, A. (2020),
Leveraging phylogenetic trees to assess variability of reservoir models (pdf),
in: Gonzalez, K., Hincapie, R., Pastor, P. and Valbuena, E., eds.,
Proceedings of 2020 Society of Petroleum Engineers (SPE) Virtual Latin American
and Caribbean Petroleum Engineering Conference, OnePetro Online Library,
OnePetro, Richardson, Texas, SPE-199099-MS, 11 pages.