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Fibre-generated point processes and fields of orientations
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Hill, Bryony J., Kendall, Wilfrid S. and Thönnes, Elke. (2012) Fibre-generated point processes and fields of orientations. Annals of Applied Statistics, Vol.6 (No.3). pp. 994-1020. ISSN 1941-7330
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Abstract
This paper introduces a new approach to analyzing spatial point data clustered along or around a system of curves or "fibres." Such data arise in catalogues of galaxy locations, recorded locations of earthquakes, aerial images of minefields and pore patterns on fingerprints. Finding the underlying curvilinear structure of these point-pattern data sets may not only facilitate a better understanding of how they arise but also aid reconstruction of missing data. We base the space of fibres on the set of integral lines of an orientation field. Using an empirical Bayes approach, we estimate the field of orientations from anisotropic features of the data. We then sample from the posterior distribution of fibres, exploring models with different numbers of clusters, fitting fibres to the clusters as we proceed. The Bayesian approach permits inference on various properties of the clusters and associated fibres, and the results perform well on a number of very different curvilinear structures.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Faculty of Science > Statistics |
| Library of Congress Subject Headings (LCSH): | Point processes, Bayesian statistical decision theory |
| Journal or Publication Title: | Annals of Applied Statistics |
| Publisher: | Institute of Mathematical Statistics |
| ISSN: | 1941-7330 |
| Date: | 2012 |
| Volume: | Vol.6 |
| Number: | No.3 |
| Page Range: | pp. 994-1020 |
| Identification Number: | 10.1214/12-AOAS553 |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Access rights to Published version: | Restricted or Subscription Access |
| Related URLs: | |
| References: | ADLER, R. J. and TAYLOR, J. E. (2007). Random Fields and Geometry. Springer, New York. MR2319516 ARSIGNY, V., FILLARD, P., PENNEC, X. and AYACHE, N. (2006). Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magn. Reson. Med. 56 411–421. AUGUST, J. and ZUCKER, S. W. (2003). Sketches with curvature: The curve indicator random field and Markov processes. IEEE Trans. Pattern Anal. Mach. Intell. 25 387–400. BARROW, J. D., BHAVSAR, S. P. and SONODA, D. H. (1985). Minimal spanning trees, filaments and galaxy clustering. Royal Astronomical Society, Monthly Notices 216 17–35. BROOKS, S. P. and ROBERTS, G. O. (1998). Convergence assessment techniques for Markov chain Monte Carlo. Statist. Comput. 8 319–335. DRYDEN, I. L., KOLOYDENKO, A. and ZHOU, D. (2009). Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging. Ann. Appl. Stat. 3 1102–1123. MR2750388 GENOVESE, C. R., PERONE-PACIFICO, M., VERDINELLI, I. andWASSERMAN, L. (2009). On the path density of a gradient field. Ann. Statist. 37 3236–3271. HILL, B. J. (2011). An orientation field approach to modelling fibre-generated spatial point processes. Ph.D. thesis, Univ.Warwick. Available at http://www2.warwick.ac.uk/go/ethonnes/fibres/ Hill.pdf. ILLIAN, J., PENTTINEN, A., STOYAN, H. and STOYAN, D. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. Wiley, New York. KASPI, H. and MANDELBAUM, A. (1994). On Harris recurrence in continuous time. Math. Oper. Res. 19 211–222. MR1290020 LE BIHAN, D. L., MANGIN, J. F., POUPON, C., CLARK, C. A., PAPPATA, S., MOLKO, N. and CHABRIAT, H. (2001). Diffusion tensor imaging: Concepts and applications. J. Magn. Reson. Imaging 13 534–546. LI, C., SUN, X., ZOU, K., YANG, H., HUANG, X., WANG, Y., LUI, S., LI, D., ZOU, L. and CHEN, H. (2007). Voxel based analysis of DTI in depression patients. International Journal of Magnetic Resonance Imaging 1 43–48. MARTÍNEZ, V. J. and SAAR, E. (2002). Statistics of the Galaxy Distribution. Chapman & Hall/CRC, Boca Raton, FL. MØLLER, J. andWAAGEPETERSEN, R. P. (2004). Statistical Inference and Simulation for Spatial Point Processes. Monographs on Statistics and Applied Probability 100. Chapman & Hall, London. MR2004226 PEARL, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo. MR0965765 STANFORD, D. C. and RAFTERY, A. E. (2000). Finding curvilinear features in spatial point patterns: Principal curve clustering with noise. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 601–609. STOICA, R. S., MARTÍNEZ, V. J. and SAAR, E. (2007). A three-dimensional object point process for detection of cosmic filaments. Appl. Statist. 56 459–477. STOICA, R. S., MARTÍNEZ, V. J. and SAAR, E. (2010). Filaments in observed and mock galaxy catalogues. Astronomy and Astrophysics 510 1–12. STOICA, R. S.,MARTÍNEZ, V. J.,MATEU, J. and SAAR, E. (2005). Detection of cosmic filaments using the Candy model. Astronomy and Astrophysics 434 423. STOYAN, D., KENDALL, W. S. and MECKE, J. (1995). Stochastic Geometry and Its Applications, 2nd ed. Wiley, New York. SU, J. (2009). A tensor approach to fingerprint analysis. Ph.D. thesis, Univ. Warwick. SU, J., HILL, B. J., KENDALL, W. S. and THÖNNES, E. (2008). Inference for point processes with unobserved one-dimensional reference structure. CRiSM Working Paper 8-10, Univ. Warwick. WATSON, C. (2001). NIST special database 30: Dual resolution images from paired fingerprint cards. National Institute of Standards and Technology, Gaithersburg. |
| URI: | http://wrap.warwick.ac.uk/id/eprint/38081 |
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