Abstract
Spatiotemporal reasoning involves pattern recognition in space and time. It is a complex process that has been dominated by manual analytics. In this chapter, we explore the new method that combines computer vision, multi-physics simulation and human-computer interaction. The objective is to bridge the gap among the three with visual transformation algorithms for mapping the data from an abstract space to an intuitive one, which includes shape correlation, periodicity, cellular shape dynamics, and spatial Bayesian machine learning. We tested this approach with the case studies of tracking and predicting oceanographic objects. In testing with 2,384 satellite image samples from SeaWiFS, we found that the interactive visualization increases robustness in object tracking and positive detection accuracy in object prediction. We also found that the interactive method enables the user to process the image data at less than 1 min per image versus 30 min per image manually. As a result, our test system can handle at least ten times more data sets than traditional manual analysis. The results also suggest that minimal human interactions with appropriate computational transformations or cues may significantly increase the overall productivity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bandini, S. and Pavesi, G. (2002) Controlled generation of two-dimensional patterns based on stochastic cellular automata, Future Generation Computer Systems, 18: 973–981
Blake, R. (1993) Cats perceive biological motion, Psychol. Sci. 4: 54–57
Chen, J., Silver, D. and Liang, J. (2003) The Feature Tree: Visualizing Feature Tracking in Distributed AMR Datasets. In: IEEE Parallel Visualization and Graphics Symposium
Correa, C., Silver, D. and Chen, M. (2006) Feature Aligned Volume Manipulation for Illustration and Visualization, IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization/Information Visualization 2006), 12(5)
Cowell, A. J., Havre, S., May, R. and Sanfilippo, A. (2005) Scientific Discovery Within Data Streams, Ambient Intelligence for Scientific Discovery, Vol. 3345, Springer
Essam, J. W. (1980) Percolation theory, Rep. Prog. Phys. 43: 833
Ferguson, E. S. (1977) The minds eye: Non-verbal thought in technology, Science 197: (4306)827–836
Ferguson, E. S. (1992) Engineering and the mind’s eye. Cambridge, MA: MIT Press
Fox, R.McDaniel, C. (1982) The perception of biological motion by human infants, Science 218: 486–487
Hardy, J. et al. (1976): 1949–1961 Molecular dynamics of a classical lattice gas: Transport properties and time correlation functions, Phys. Rev. A 13(5)1949–1961
Hubona, G. S. and Shirah, G. W. (2005) Spatial Cues in 3D Visualization, Ambient Intelligence for Scientific Discovery, Vol. 3345, Springer
Johansson, G. (1973) Visual perception of biological motion and a model for its analysis, Percept. Psychophys. 14: 201–211
Kumar, B. V. K. V., Mahalanobis, A. and Juday, R. D. (2005) Correlation Pattern Recognition, University PressCambridge
Lewin, K. (1975) Spatial Cues in 3D Visualization, Ambient Intelligence for Scientific Discovery, Greenwood Press, Westport
Leyton, M. (1992) Symmetry, Causality, Mind, MIT Press.
Pryke, A. and Beale, R. (2005) Interactive Comprehensible Data Mining, Ambient Intelligence for Scientific Discovery, Vol. 3345, Springer
D. K. Rossmo, (1999) Geographical Profiling, CRC Press
Sethares, W. A. and Staley, T. W. (1999)Periodicity transforms, IEEE Transact. Signal Process. 47: (11)2953–2962
Sethares, W. A. and Staley, T. W. (2001) Meter and periodicity in musical performance, J. New Music Res. 22: (5)1–11
Sonka M., Boye, R., and Hlavac, V. (1998) Image processing, Analysis and Machine Vision (snd edition). PWS Pub Co.
Stauffer, D. (1994) Introduction to Percolation Theory, Taylor and Francis
Toffoli, T. and Margolus, N. (1987) Cellular automata machines: a new environment for modeling, MIT Press
Vrolijk, B. (2007) Interactive visualisation techniques for large time-dependent data sets. PhD thesis, Delft University of Technology, The Netherlands, June 2007, ISBN 978-90-8559-293-8
Wolfram, S. (1983) Statistical mechanics of cellular automata, Rev. Mod. Phys. 55: 601–644
Wolfram, S. (1984a)Cellular automata as models of complexity, Nature 311: 419–424
Wolfram, S. (1984b) Universality and complexity in cellular automata, Physica D 10: 1–35
Zhou, S., Chellappa, R. and Moghaddam, B. (2005) Tracking and recognition using apprearance-adaptive models in particle filters, IEEE Trans. Image Process. 13: (11)1491–1506
Acknowledgments
This study is supported by NASA ESTO grant AIST-QRS-04-3031 and National Science Foundation grant CT-ER 0716657. The authors appreciate the comments and suggestions from Karen Meo, Kai-Dee Chu, Steven Smith, Gene Carl Feldman and James Acker from NASA. Also, many thanks to Professors Christos Faloulus and William Eddy of Carnegie Mellon University for their input. The authors also thank Brenda Battad for her text editing.
Editor information
Editors and Affiliations
Appendices
Appendix
Cellular Shape Dynamics Simulation Rules
The growth rule is as followed given the growth amount N:
-
1.
Pick an arbitrary cell neighboring the region of life
-
2.
If the sum of neighboring cells 2 and the arbitrary cell is “0” then make the cell “1”
-
3.
Repeat steps 1 and 2 until the desired N has been reached
The shrink rule is as followed given the shrink amount M:
-
1.
Pick an arbitrary cell on the edges of the region of life
-
2.
If the sum of neighboring cells 2 and the arbitrary cell is “1” then make the cell “0”
-
3.
Repeat steps 1 and 2 until the desired N has been reached, or a maximum iteration threshold has been reached.
-
4.
If the threshold is reached, it means the region of life has shrunk to a very condensed region and will not easily reduce in size.
The collision rule is as followed given elasticity E:
-
1.
If any cells cross the boundary, proceed to the next step.
-
2.
Treat the rows and columns outside of the boundary as “1”
-
3.
If the sum of neighboring cells around any cell is “4” then make the cell “1.” This phase will add the cells onto the boundary row, or in the case that the region of life has already collided with a boundary, add cells close to the previously collided cells.
-
4.
Repeat this step E times since the more elastic the collision, the more the cells will spread.
The wind translation rules are as followed given wind speed W and direction:
-
1.
Using a constant for speed of translation with relationship to wind speed.
-
2.
Move the shape at the speed of one iteration at a time in the x and y direction.
-
3.
If there is a collision with a boundary, spread along the boundary, but continue moving.
Rights and permissions
Copyright information
© 2009 Springer-Verlag London Limited
About this chapter
Cite this chapter
Cai, Y., Stumpf, R., Tomlinson, M., Wynne, T., Chung, S., Boutonnier, X. (2009). Interactive Spatiotemporal Reasoning. In: Liere, R., Adriaansen, T., Zudilova-Seinstra, E. (eds) Trends in Interactive Visualization. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84800-269-2_14
Download citation
DOI: https://doi.org/10.1007/978-1-84800-269-2_14
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-84800-268-5
Online ISBN: 978-1-84800-269-2
eBook Packages: Computer ScienceComputer Science (R0)