پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شمارهها | 6,228 |
| تعداد مقالات | 67,750 |
| تعداد مشاهده مقاله | 115,085,793 |
| تعداد دریافت فایل اصل مقاله | 89,824,595 |
Non-rigid star pattern recognition for preparation of IOD’s observations | ||
| Earth Observation and Geomatics Engineering | ||
| مقاله 4، دوره 1، شماره 1، شهریور 2017، صفحه 47-57 اصل مقاله (1.5 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22059/eoge.2017.220378.1005 | ||
| نویسندگان | ||
| Mona Kosary* ؛ Saeed Farzaneh | ||
| School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran. | ||
| چکیده | ||
| The invention of electro-optical devices at the beginning of the 21st century was really a rebirth in the geodetic astronomy. Today, the digital cameras with relatively high geometric and radiometric accuracy have opened a new insight in the satellite attitude determination and the study of the Earth's surface geometry and physics of its interior, i.e., the computation of astronomical coordinates and the vertical deflection components. In the automatic star detection, high precision and reliability in extraction of the star's centers from the captured images and corresponding them with the astronomical coordinates is the most important point. In this article, the probabilistic method has been applied for the star matching. The registration is treated as a Maximum Likelihood estimation problem with the motion constraint over the velocity field such that the catalogue coordinates set moves coherently to align with the pixels coordinates set. The motion coherence has been constrained to the matching problem and derives a solution of regularized ML estimation through the vibrational approach, which leads to an elegant kernel form. In this way, the EM algorithm has been applied for the penalized ML optimization with deterministic annealing. This method finds correspondence between stars coordinates in catalogue and image without making any prior assumption of the transformation model except the motion coherence. This method can estimate the gnomonic transformations between the catalogue and the image and is shown to be accurate and robust in the presence of image noise and outliers. The result of evaluation by proposed algorithm on the image taken by the TZK2-D camera, indicated that the point matching is achieved by standard deviation less than 0.001 pixel. | ||
| کلیدواژهها | ||
| Star matching؛ Initial orbit determination؛ Coherent motion theory | ||
| مراجع | ||
|
Available from: http://www.aiub.unibe.ch/index_eng.html. Montenbruck, O. & Gill. E. (2000). Satellite orbits. Springer,. 2: p. 257-291. Amann, M., et al. (2001). Laser ranging: a critical review of usual techniques for distance measurement. Optical engineering, 40(1): p. 10-19. Hejduk, M., et al. (2004). Satellite detectability modeling for optical sensors. in AMOS Technical Conference. Li, C., et al. (2014). Performance analysis of visible light communication using the STBC-OFDM technique for intelligent transportation systems. International Journal of Electronics,.101(8): p. 1117-1133. Hirt, C. & Burki, B. (2006). Status of geodetic astronomy at the beginning of the 21st century. Hirt, C. & Flury, J. (2008). Astronomical-topographic levelling using high-precision astrogeodetic vertical deflections and digital terrain model data. Journal of Geodesy, 82(4): p. 231-248. Stoveken, E. & Schildknecht, T. (2005). Algorithms for the optical detection of space debris objects. in Proceedings of the 4th European Conference on Space Debris, Darmstadt, Germany. Salomon, P. & Goss, W. (1976). A microprocessor-controlled CCD star tracker, in 14th Aerospace Sciences Meeting. Junkins, J.L. & T.E. Strikwerda (1979). Autonomous star sensing and attitude estimation. in Guidance and Control Conference. Junkins, J.L., C.C. White, & J.D. Turner (1977). Star pattern recognition for real-time attitude determination. Journal of the Astronautical Sciences, 25(3): p. 251-270. Clouse, D.S. & C.W. Padgett (2000). Small field-of-view star identification using Bayesian decision theory. IEEE Transactions on Aerospace and Electronic Systems, 36(3): p. 773-783. Chen, C., B. Wang, & C. Zhang (2004). Star pattern recognition algorithm based on main star pair. Computer Simulation, 21(6): p. 31-53. Jiancheng, F., Q. Wei, & M. Xiaohong (2005). All-sky autonomous star map identification algorithm based on Delaunay triangulation cutting algorithm. Journal of Beijing University of Aeronautics and Astronautics, 31: p. 312-315. Padgett, C. & K. Kreutz-Delgado (1977). A grid algorithm for autonomous star identification. IEEE Transactions on Aerospace and Electronic Systems, 33(1): p. 202-213. Li, L.H.Z., F. E. & Lin, T. (2000). An all-sky autonomous star map identification algorithm based on genetic algorithm. Opto-Electronic Engineering, 27: p. 15-18. Paladugu, L., M.P. Schoen, & B.G. Williams (2003). Intelligent techniques for star-pattern recognition. in ASME 2003 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers. Spratling, B.B. & D. Mortari (2009). A survey on star identification algorithms. Algorithms, 2(1): p. 93-107. Grafarend, E. & F. Krumm (2006). Map Projection, Springer. Hoffleit, D. & C. Jaschek (1982). The bright star catalogue. The Bright Star Catalogue, New Haven: Yale University Observatory (4th edition). Perryman, M.A., et al. (1997). The HIPPARCOS catalogue. Astronomy and Astrophysics, 323. Lowe, D.G. (1999). Object recognition from local scale-invariant features. in Computer vision, 1999. The proceedings of the seventh IEEE international conference on, Ieee. Bilmes, J.A. (1998). A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. International Computer Science Institute, 4(510): p. 126. Langley, P., W. Iba, & K. Thompson (1992). An analysis of Bayesian classifiers. in Aaai. Yuille, A.L., and N.M. Grzywacz,(1989). A mathematical analysis of the motion coherence theory. International Journal of Computer Vision, 3(2): p. 155-175. Yuille, A.L., & N.M. Grzywacz (1988). The motion coherence theory. in Computer Vision., Second International Conference on, IEEE. Girosi, F., M. Jones, & T. Poggio (1995). Regularization theory and neural networks architectures. Neural computation, 7(2): p. 219-269. Chui, H. & A. Rangarajan (2003). A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding, 89(2): p. 114-141. Torr, P.H. & A. Zisserman (2000). MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78(1): p. 138-156. Perona, P. & J. Malik (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7): p. 629-639. | ||
|
آمار تعداد مشاهده مقاله: 280 تعداد دریافت فایل اصل مقاله: 305 |
||