Classical Approaches of Informatics to Astronomical Images Processing

1. a presentation by Dimo T. Dimov1 (dtdim@iinf.bas.bg ) in collaboration with Milcho Tsvetkov2 and Yuliana Goranova2 Classical Approaches of Informatics to Astronomical Images Processing 1Institute of Information Technologies at Bulgarian Academy of Science (IIT-BAS) 2Institute of Astronomy at Bulgarian Academy of Science (IA-BAS) Sofia 7th Bulgarian-Serbian Astronomical Conference June 01-04, 2010, Chepelare, Bulgaria

2. Acknowledgements This work was supported by following grants of the Institute of Information Technologies (IIT) at Bulgarian Academy of Sciences (BAS): Grant # DO-02-275/2008 of the National Science Fund at Bulgarian Ministry of Education & Science, and Grant # 010088/2007 of BAS

3. Some Abbreviations ADBI = Astronomical DB of Images FITS = Flexible Image Transfer System HT = Hough Transform EFIRS = Experimental Fast Image Retrieval System

4. Contents 1. EFIRS – an instrumental system used for our demonstrations 2. ADBI: some types of astro-images of interest - star chain plate images - Carte-du-Ciel plate images - astro-images of lost positioning Hough transform to stress on stretched objects in plate images - (,)HT, it is equal to a 2D Radon transform - (,)HT applied for astro-images of interest - Plate images preprocessing: adaptive binarization - Exact HT performance for the both given grids: the input grid and the chosen grid for HT output 4. Registrationof astro-images: “lost-in-space” type (vector type or grid based algorithm) 5. Discussion & Conclusion

5. References • 1. Kounchev, O., Tsvetkov, M., Dimov, D., et al.: 2009, Bulg. J. Phys.,32/2. • 2. Aniol, R., Duerbeck, H.W., Seitter, W.C., Tsvetkov, M.K. 1990, IAU Symp. • 137,Kluwer Acad. Publish., Dordrecht-Boston-London, 85. • 3. Starck, J.-L., Murtagh F., 2002, Springer-Verlag, 303. • Bertin, E., Arnouts, S., 1996, Astronomy & Astrophysics Supplement Series, 117, 393. • Spratling B. B., and D. Mortari, Algorithms J., 2 (2009), 93-107 • (www.mdpi.com/journal/algorithms) • Kolomenkin, M., Polak, S., Shimshoni, I., and Lindenbaum M., (2008) • (http://mis.hevra.haifa.ac.il/~ishimshoni/papers/StarTracker.pdf)

6. input image image content to a key index key access Image DB of PORB EFIRS DBMS … … Result of EFIRS operation Original images from the Patent Office of Republic of Bulgaria (PORB)

10. HT of a point

11. HT of 2 points

12. HT of 4 points

13. HT of a line (the points of a line)

14. HT of imagination of lines (text rows)

15. HT of a chain star image See EFIRS experiment (No.26) For more detail, see: Dimov, D., K. Tsvetkova, M. Tsvetkov, A. Kolev, Hough Transform Approach to Flare Stars’ Identification in Chain Plate Images, Serdica J., Sofia, (to appear, 2010)

16. HT of a Carte-du-Ciel plate image See EFIRS experiment (No.26)

17. Necessary refinements of HTused for astronomical applications Preliminary binarization of input images, i.e. segmentation in 2 levels of intensity: object(s) & background A locally adaptive and data driven binarization to suppress different “lightening conditions” and/or artifacts in plate images For more detail, see: Dimov, D., and A. Dimov, Data Driven Approach to Binarization of Astronomical Images, in Proceedings of CompSysTech’2010, Sofia, Bulgaria, (to appear) (2) Exact HT performance for the both given grids: the input astro-image grid and the chosen grid of HT accumulation space. - the basic idea is briefly shared in next 2-3 slides - the currently developed software explores an approximation of above idea; it’s expected that the operation efficiency (processing speed and preciseness) will be much better. For more detail, see: Dimov, D., Exact Performance of (,)-Hough Transform for Star Chain Images Processing, to be presented at CTF’2010, 04-10.06.2010, Sozopol, Bulgaria

18. Locally Adaptive Data Driven Binarization of Astro Images A part of Pleiades (For better visibility) A binarization by an adaptive threshold surface A binarization by a global optimal threshold (as a negative example)

19. Locally Adaptive Data Driven Binarization… The division map for binarization by parts A threshold surface calculated for the division map A demo is available. For more detail see: Dimov, D., and A. Dimov, Data Driven Approach to Binarization of Astronomical Images, in Proceedings of CompSysTech’2010, Sofia, Bulgaria, (to appear)

20. Exact HT performance for the both given grids: (the input grid and the chosen grid for HT output) Basic types for the trapezium-like hexagon TC6, i.e. the arbitrary vertical section of Cosine-shape of a given image pixel.

21. Registrationof astro-images • Registration or positioning towards a stellar catalog. A similar task: for star trackers, cf. the survey by Spratling and Mortari (2009), as well as Kolomenkin et al (2008). • Algorithm types: “lost-in-space”, recursive (tracking) and non-dimensional ones. • In our case (archive plate images), we should most of all interested in application/modification of “lost-in-space” algorithms. This is the most general case, nevertheless of not being the most often one. • We have not to obey strong limitations for real time processing and/or for computing energy consumption that are typical for star trackers of cosmic installation. • Possible decisions: (1) Search-Less Algorithm (SLA)of Mortari (star 3-angles based) (2) Lost-in-space algorithm of Kolomenkin (“star distances” based) (3) Grid algorithm based on CBIR techniques (an idea still under construction).

22. input image image content to a key index key access Image DB of PORB EFIRS DBMS … … Result of EFIRS operation Original images from the Patent Office of Republic of Bulgaria (PORB)

23. EFIRS’sBackground (for AstroInformatics) - a visual comparison of E1, E2 & E3 keys of EFIRS - a promising result for an image of Pleiades An original mark image. Log2DFT-SPM-FWT-keyE1 Log2DFT-SPM-FCsT-keyE3 Log2DFT-SPM-FFT-keyE2

24. EFIRS’sBackground (continues…)for more details, see “ADMKD'07 Varna_.ppt”(e.g. slides #25  #28)And, it’s better to see a demo 

25. A Brief Demonstration of EFIRS: • If the Demo is not possible show next 4 slides: • EFIRS: the IDB browser • EFIRS: the Search-engine start menu • EFIRS: the Search-engine (result table I) • EFIRS: the Search-engine (result table II)

26. EFIRS: the IDB browser

27. EFIRS: the Search-engine start menu

28. EFIRS: the Search engine (result table I)

29. EFIRS: the Search engine (result table II)

30. Discussion & Conclusions • Discussion ? • Possible conclusion – HT is promising tool for flare object identification in star-chain plate images • Possible conclusion – HT can be also used for auxiliary measuring lines determination in Carte-du-Ciel plate images • Which type of lost-in-space algorithm to chose for archive plate images registration

31. Thank You!

32. How EFIRS operates with astro-images, now 3 images of experiment: BEL016A000076 (Orion), BEL016A000134 (Pleiades), and ROZ050 000046 (centre). 2D Wavelets: (MatLab similarities ) 2D Fourier transform: Simple Polar Mapping (SPM), Log Polar Mapping (LgPM): PFWT (Polar Fourier Wavelet Transform): similarities with the Fourier-Mellin transform Generation of EFIRS image keys to ADBI Binarization experiment: an aim: to locate potential star centers Contouring: not very promising, eventually to isolate (manually ?) damaged regions of a plate Zoom: ? a demo tour in Pleiades ? Hough/Radon transform: (see ROZ050 000046 (centre) )?