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Meyer Pesenson, William Roby, George Helou,

Image Processing Application for Cognition (IPAC) Traditional and Emerging Topics in Image Processing in Astronomy. Meyer Pesenson, William Roby, George Helou, Bruce McCollum, Loi Ly, Xiuqin Wu, Seppo Laine, Booth Hartley Spitzer Science Center, IPAC, California Institute of Technology.

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Meyer Pesenson, William Roby, George Helou,

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  1. Image Processing Application for Cognition (IPAC) • Traditional and Emerging Topics in • Image Processing in Astronomy Meyer Pesenson, William Roby, George Helou, Bruce McCollum, Loi Ly, Xiuqin Wu, Seppo Laine, Booth Hartley Spitzer Science Center, IPAC, California Institute of Technology

  2. Dual Nature of Human Visual Perception “Mine eye is in my mind” W. Shakespeare Goal of Image Processing “To serve their eyes, and in it put their mind” W. Shakespeare ADASS XVII, London, September 25, 2007

  3. Astronomical Community’s Growing Awareness of the Importance of Image Processing • Featured themes of ADASS 2007 • Image Processing and Data Visualization • Algorithms and Image Processing • More groups developing/utilizing modern methods of IP • France, UK (J-L. Stark, E. Bertin, F. Murtagh, S.Marshall et al.) • Harvard Initiative in Innovative Computing (M. Borkin, A. Goodman, et al.) • INAF, CENECA (U. Becciani, C. Gheller et al. ) • Harvard-Smithsonian Center for Astrophysics (E. Bressert, P. Edmonds) • Space Telescope Science Institute (W. Hack) • More works published on Automated extraction of features from images ADASS XVII, London, September 25, 2007

  4. Motivation Image processing framework which will facilitate: • Automated extraction of objects (PS, arcs, etc.) • Analysis of the morphology of diffuse structures • Astrometry • Quality assessment ADASS XVII, London, September 25, 2007

  5. Presentation Outline • What Image Processing can do (some real and simulated images processed via the Framework) • Automated and semi-automated processing • Image Quality Assessment • Demo ADASS XVII, London, September 25, 2007

  6. Conventional Methods of Image Processing • Applying conventional smoothing methods is not sufficient for multi-scale astronomical images • They eliminate faint sources and smear more prominent ones and diffuse structures ADASS XVII, London, September 25, 2007

  7. Methods based on Nonlinear Partial Differential Equations (PDEs) • Preserve both, diffuse structures and imbedded point sources, thus facilitating PS extraction • Facilitate detection of transients • Reveal multi-filamentary structures • Facilitate detecting outflows from young stars in star forming regions ADASS XVII, London, September 25, 2007

  8. w28, Chandra ( courtesy of J. Rho, SSC, Caltech)Left: raw; Right: processed by using a nonlinear PDE ADASS XVII, London, September 25, 2007

  9. NGC 2775Left - noisy image; Right - processed by using a nonlinear PDE ADASS XVII, London, September 25, 2007

  10. NGC 5962 ADASS XVII, London, September 25, 2007

  11. NGC 5962Left-convolved with a Gaussian; Right - a nonlinear PDE ADASS XVII, London, September 25, 2007

  12. IC 405, SPITZER, IRAC 8.0 µm(S. McCandliss)Filaments and a bow-shock near HD 34078( A.Noriega-Crespo, et.al. AJ, 113:780, 1997; K.France et. al. AJ, 655:920, 2007 ) ADASS XVII, London, September 25, 2007

  13. IC 405, SPITZER, IRAC 8.0 µm UnveilingFine Structure - flux change rateFacilitates analysis of Nebular Morphology, Jets, Embedded Sources, Shock Fronts. ADASS XVII, London, September 25, 2007

  14. IC 405, SPITZER, IRAC 8.0 µm Front of the bow-shock wave(left- flux change rate; right- it’s angle) ADASS XVII, London, September 25, 2007

  15. NGC 4594 (NED b2) ADASS XVII, London, September 25, 2007

  16. NGC 4594 (NED b2)Morphology ADASS XVII, London, September 25, 2007

  17. HH34 (center) SPITZER, IRAC 8.0 µm (A. Noriega-Crespo)(Reipurth, et.al., AJ, 123:362, 2002) ADASS XVII, London, September 25, 2007

  18. HH34 (center) SPITZER, IRAC 8.0 µm(A. Noriega-Crespo)Morphology(NLD15_02) ADASS XVII, London, September 25, 2007

  19. NGC 7023, SPITZER, IRAC 3.6µm (J. Houck)Reflection Nebulae ADASS XVII, London, September 25, 2007

  20. NGC 7023, SPITZER, IRAC 3.6µm Morphology ADASS XVII, London, September 25, 2007

  21. Perseus, SPITZER, MIPS 70µm (N.Evans) ADASS XVII, London, September 25, 2007

  22. Perseus, SPITZER, MIPS 70µm (N.Evans)Reveals Morphology of the Filaments and the underlying coverage map ADASS XVII, London, September 25, 2007

  23. Simulated Image , SNR=3”Point sources” in the middle of each square; In the middle row, from left to right: I. Elliptic Galaxy (horizontally oriented; Hubble Law): ampl=4, length=10; II. Elliptic Galaxy (vertically oriented; de Vaucouleurs Law): ampl=30, length=10; ADASS XVII, London, September 25, 2007

  24. Extracting boundaries from a Noisy ImageLeft - without pre-processing; Right - after convolving with a Gaussian; Bottom - after preprocessing by using a nonlinear diffusion equation ADASS XVII, London, September 25, 2007

  25. Noisy Image Convolved with a Gaussian = 3 The noise is reduced, but the boundaries and especially tiny point sources became blurry. ADASS XVII, London, September 25, 2007

  26. Image Processed by a Nonlinear PDE(zoomed in; the Globular cluster is in the middle) ADASS XVII, London, September 25, 2007

  27. There is more to Image Processing than Meets the Eye - Automated vs. Interactive Feature Extraction • It is essential to distinguish between two very different objectives of pre-processing: • to prepare grounds for Automatedextraction of features (segmentation) • to facilitate further Interactive analysis ADASS XVII, London, September 25, 2007

  28. Large Imaging Surveys andAutomated Search • “automated search for gravitational lenses will be essential in analyzing imaging datasets from future telescopes” R. Blandford et.al. “An Automated Search for Gravitational Lenses in the HST Imaging Archive” (2006) ADASS XVII, London, September 25, 2007

  29. Next Level Processing (I) • Automatic detection of artifacts of instruments and pipeline processing (lines, squares, etc.) • Detection of Point Sources ADASS XVII, London, September 25, 2007

  30. Next Level Processing (II) • AutomaticDetection of Diffuse Structures • elliptic galaxies, ring Nebulas, SNRs, arcs, etc. • Morphology and Classification • pattern matching techniques, image matching, content-based image retrieval from pictorial databases, etc. ADASS XVII, London, September 25, 2007

  31. NGC 2264, SPITZER, IRAC 8.0 µm Detecting Artifacts ADASS XVII, London, September 25, 2007

  32. NGC 2264Detecting Artifacts (cont.) Such processing can be used for quick QA screening and then flagging “suspicious” images ADASS XVII, London, September 25, 2007

  33. Image Quality Assessment (iQA) • Simply implementing IP algorithms is not sufficient • iQA must be an essential part of any IP application: • to dynamically optimize processing modules. • to monitor and evaluate image quality. ADASS XVII, London, September 25, 2007

  34. Astronomical Data and iQA • Astronomical data volumes are increasing rapidly - LSST alone will produce 13 TB of data every 8 hours of observing (~ 450MB/s) • Manual image QA for such sets will fail ADASS XVII, London, September 25, 2007

  35. Future Topics for QA Existing QA in astronomy does not address the complete scope of the problem - • Goal of IP is better images, but “better” is rarely defined • Future work should establish objective metrics consistent with subjective human evaluation of astronomical images ADASS XVII, London, September 25, 2007

  36. Integration of Image Processing, Visualization and Quality Assessment Analyzing of vast data sets demands for a newapproach based on integration within a single Framework of • image processing (iP) • image visualization(iV) • image quality assessment (iQA) Framework = iP + iQA + iV • Such approach is crucial for handling the large data volumes produced by upcoming projects such as PanSTARRS and LSST ADASS XVII, London, September 25, 2007

  37. DEMO • Demo of the Framework by William Roby ADASS XVII, London, September 25, 2007

  38. J.L. Starck, F. Murtagh, “Astronomical Image and Data Analysis,” Springer, 2002. M. Pesenson, et al., Image Segmentation and Denoising based on Shrira-Pesenson Equation, ADASS, 2004. J. Ingalls, et.al., morphology and Colors of Diffuse Emission in the SPITZER Galactic First Look Survey, AJ, 154:281, 2004. F. Lenzen, S. Schindler, “Automatic detection of arcs …”, AA, v. 416, 2004. M. Pesenson, D. Makovoz, W. Reach, et.al. Astronomical point-source detection based on nonlinear image filtering with PDEs, SPIE, 2005. M. Pesenson,, W. Roby, et al., Image Processing Application in Java, ADASS, 2006. U. Becciani, M.Comparato, A. Costa, C. Gheller, et al., VisIVO a tool for the VO, ADASS, Tucson, Oct. 2006. M. Borkin, A. Goodman, Application of Medical Imaging Software to 3D Visualization of Astronomical Data, ADASS 2006. E. Bressert, P. Edmonds, A New Image Denoising Algorithm that Preserves Morphology of Astronomical Data, AAS 2007.

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