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Using Image Data in Your Research

Using Image Data in Your Research

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Using Image Data in Your Research

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  1. Using Image Data in Your Research Kenton McHenry, Ph.D. Research Scientist

  2. Image and Spatial Data Analysis Group

  3. Image and Spatial Data Analysis Group • Research & Development • Cyberinfrastructure: Software development for the sciences (and industry) • Computer Vision: Information from images • High Performance Computing: Software that scales with regards to computation and data

  4. Image and Spatial Data Analysis Group • Content Based Retrieval • Search in digitized collections • Document segmentation • Authorship • 3D models • Automatic Image Annotation • Assign keywords as metadata • Tracking • 3D Reconstruction • Image Stitching

  5. Image and Spatial Data Analysis Group • Digital Preservation • Access to data content independent of format • Access to software functionality independent of distribution • Information loss evaluation • Document similarity • Environmental Modeling • Workflows • Heterogeneous data sources • Data Exploration • Data mining • eScience

  6. Goals for Today • A high level understanding of what Computer Vision is and how YOU might use it. • A sense of what is currently possible • A sense of how these things break • A sense of what might be possible • A sense of what is pure science fiction! • The looming opportunity in “Big Data” • A little bit of hands on experience

  7. Computer Vision • Books: • D. Forsyth, J. Ponce, “Computer Vision: A Modern Approach”, Pearson, 2011. • R. Szeliski, “Computer Vision: Algorithms and Applications”, http://szeliski.org/Book, 2010. • CS 543: Computer Vision (UIUC) • Derek Hoiem, Ph.D. • http://www.cs.illinois.edu/class/sp12/cs543

  8. Computer Vision [Hoiem, 2012]

  9. Computer Vision • Make a computer understand images and video • What kind of scene? • Are there cars? • Where are the cars? • Is it day or night? • What is the ground made of? • How far is the building? [Hoiem, 2012]

  10. Raster Images [Hoiem, 2012]

  11. Image Creation Light emitted Fraction of light reflects into camera Lens Sensor [Hoiem, 2012]

  12. Image Creation • Light(s) • Position • Strength • Geometry • Color • Surface(s) • Orientation • Color • Material • Nearby surfaces • Sensor • Lens • Aperture • Exposure • Resolution Light emitted Light reflected to camera Sensor [Hoiem, 2012]

  13. Surfaces: Reflected Light incoming light absorption specular reflection incoming light incoming light diffuse reflection [Hoiem, 2012]

  14. Surface: Reflected Light

  15. Surfaces: Orientation 1 2 Ix = rxLNx [Hoiem, 2012]

  16. Surfaces transparency light source light source refraction [Hoiem, 2012]

  17. Surfaces light source fluorescence λ1 λ2

  18. Surfaces light source phosphorescence t=1 t>1 [Hoiem, 2012]

  19. Surfaces light source subsurface scattering λ [Hoiem, 2012]

  20. Light Human Luminance Sensitivity Function [Hoiem, 2012]

  21. Light [Hoiem, 2012]

  22. Light

  23. Light • [GIMP Demo]

  24. Sensors • Long (red), Medium (green), and Short (blue) cones, plus intensity rods [Hoiem, 2012]

  25. Sensors [Hoiem, 2012]

  26. Sensors R G B [Hoiem, 2012]

  27. Sensors: Perspective • Projecting a 3D world onto a 2D plane • Parallel lines disappear at vanishing points • Sizes appear smaller further away

  28. Surface Interactions! [Hoiem, 2012]

  29. Surface Interactions [Hoiem, 2012]

  30. Surface Interactions [Hoiem, 2012]

  31. Surfaces: Interactions

  32. Surface Interactions [Hoiem, 2012]

  33. Raster Images image(234, 452) = 0.58 [Hoiem, 2012]

  34. Individual Pixels [Hoiem, 2012]

  35. Neighborhoods of Pixels • For nearby surface points most factors do not change much • Local differences in brightness [Hoiem, 2012]

  36. Neighborhoods of Pixels [Hoiem, 2012]

  37. Neighborhoods of Pixels [Hoiem, 2012]

  38. Neighborhoods of Pixels [Hoiem, 2012]

  39. Changes in Intensity • Changes in albedo • Changes in surface normal • Changes in distance [Hoiem, 2012]

  40. Computer Vision • Make a computer understand images and video • Lots of variables are involved in the creation of an image/frame • Variables are not independent and interact • The problem is underconstraned • i.e. multiple scenes can result in the same image

  41. Optical Illusions

  42. Optical Illusions

  43. Optical Illusions

  44. Vision is Really Hard! • Vision is an amazing feat of natural intelligence • More human brain devoted to vision than anything else [Hoiem, 2012]

  45. State of the Art • From 1960’s to present…

  46. Barcodes • Optical machine readable representation of data • 1950’s http://en.wikipedia.org/wiki/Barcode

  47. Optical Character Recognition (OCR) Digit recognition, AT&T labs http://www.research.att.com/~yann/ License plate readers http://en.wikipedia.org/wiki/Automatic_number_plate_recognition • Technology to convert scanned documents to ASCII text • If you have a scanner, it probably came with OCR software [Hoiem, 2012]

  48. Biometrics Face recognition systems now beginning to appear more widelyhttp://www.sensiblevision.com/ Fingerprint scanners on many new laptops, other devices [Hoiem, 2012]

  49. Face detection • Many new digital cameras now detect faces • Canon, Sony, Fuji, … [Hoiem, 2012]

  50. Medical imaging 3D imaging, MRI, CT [Hoiem, 2012], http://en.wikipedia.org/wiki/3D_ultrasound