E Cognition User Summit2009 G Binnig Definiens

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E Cognition User Summit2009 G Binnig Definiens

  1. 1. Principles of Human Cognition Utilized for Automated Image Analysis Gerd Binnig 3.Nov.2009
  2. 2. I think therefore I am
  3. 3. Context Driven Image Analysis <ul><li>Context driven through CNL </li></ul><ul><ul><li>Object oriented </li></ul></ul><ul><ul><li>Knowledge based </li></ul></ul>context ?? YES
  4. 4. Which Object ?
  5. 5. Which Object ?
  6. 6. Context
  7. 7. Context
  8. 8. Object Oriented and Knowledge Based Thinking We have abstract knowledge about classes of objects and their relations Fork! Knife! Plate between
  9. 9. Context Objects – From the Concrete to the Vague Objects 1. CONTEXT Objects 2. CONTEXT Object
  10. 10. Context Navigation Image Plate ? ? ? ? Fork Handle Knife Context navigation - most important Handle Blade +1 0 +1 0 0
  11. 11. Context Navigation Context slowly builds up The easy and well defined “objects” first Image Plate ? ? ? ? Fork Handle Knife Handle
  12. 12. Only vague objects ?
  13. 13. Network of Objects The objects are undefined The relations are concrete
  14. 14. Cognition Network Language - CNL <ul><li>Context driven </li></ul><ul><ul><li>Knowledge based </li></ul></ul><ul><ul><li>Object based </li></ul></ul><ul><ul><li>Local </li></ul></ul><ul><li>Script based </li></ul>
  15. 15. <ul><ul><ul><li>Process Hierarchy </li></ul></ul></ul><ul><ul><ul><li>programming by multiple choice </li></ul></ul></ul><ul><ul><ul><li>. </li></ul></ul></ul><ul><ul><ul><li>Knowledge Hierarchy </li></ul></ul></ul><ul><ul><ul><li>natural formulation </li></ul></ul></ul><ul><ul><ul><li>. </li></ul></ul></ul><ul><ul><ul><li>Navigation </li></ul></ul></ul><ul><ul><ul><li>for directed local processing </li></ul></ul></ul><ul><ul><ul><li>. </li></ul></ul></ul><ul><ul><ul><li>Object Hierarchy </li></ul></ul></ul><ul><ul><ul><li>evolves in the course of processing </li></ul></ul></ul>The Elements of CNL MRS, Chessboard, Merge, Classification, .. Fork! Knife! Plate between
  16. 16. <ul><li>Context driven </li></ul><ul><ul><li>Knowledge based </li></ul></ul><ul><ul><li>Object based </li></ul></ul><ul><ul><li>Object-Pixel unification </li></ul></ul><ul><ul><li>Local </li></ul></ul><ul><li>Script based </li></ul><ul><li>Multi-Image capability </li></ul><ul><li>Up to 4 Dimensions </li></ul>The Elements of CNL – Version 8
  17. 17. <ul><ul><ul><li>Process Hierarchy </li></ul></ul></ul><ul><ul><ul><li>programming by multiple choice </li></ul></ul></ul><ul><ul><ul><li>. </li></ul></ul></ul><ul><ul><ul><li>Knowledge Hierarchy </li></ul></ul></ul><ul><ul><ul><li>natural formulation </li></ul></ul></ul><ul><ul><ul><li>. </li></ul></ul></ul><ul><ul><ul><li>Navigation </li></ul></ul></ul><ul><ul><ul><li>for directed local processing </li></ul></ul></ul><ul><ul><ul><li>. </li></ul></ul></ul><ul><ul><ul><li>Object Hierarchy </li></ul></ul></ul><ul><ul><ul><li>evolves in the course of processing </li></ul></ul></ul>The Elements of CNL – V8 MRS, Chessboard, Merge, Classification, .. Region O + P
  18. 18. Real Space / Size of Objects www.definiens.com www.definiens.com 1nm 1  m 1mm 10 100 10 100 0.1 100 1m 10 1Km Atom transistor Organ Person Ship Car House City Cell Forest <ul><ul><li>m=meter </li></ul></ul>Traveling through the Dimensions of Space 100
  19. 19. Phase Contrast Mic. Cells 3D-Confocal Microscopy Cell biology 3D-Confocal Microscopy Tissue Molecular Pathology 3D PET/CT Small animal Biological Research and Drug Development CT Mouse High Content Sreening Cells Proliferation index Tissue Cancer Biomarker Tissue
  20. 20. Biopsy Tissue Clinical Applications Serum Cells X-Ray Mammography CT Organs MRI low res. Organs CT Head/Neck MRI Ventricles CT Lymph Nodes
  21. 21. Context Driven Processing – Cell Cultures -1 +1 3. Micro- Nucleus Context Objects : Nuclei Cells 1. Nuclei 2. Cells Image
  22. 22. Context Objects – Blue, and Red Areas; Nucleus, Cell Blue stain red stain Different stains – different contrasts Blue area Red area -1 -1 Nucleus Cell Membrane +1 -1 Nuc. Cell Membrane +1 -1 Context Driven Processing – Tissue
  23. 23. Spinal Cord +1 Context Driven Processing – CT Spine Context Objects : Spinal cord Liver Kidney Spleen Spine
  24. 24. Solution: Anatomical Context Anatomy segmentation Context-free lymph node segmentation produces many false positives Reduced false positive rate + =
  25. 26. Hannover – University – Detecting HOUSES
  26. 27. Screenshots from Internet (Google)
  27. 28. Hannover – University – Detecting HOUSES What is easy and well defined? ..and can be used as context?
  28. 29. Meadow Potential shadow of tree 0 Context Driven Processing – Earth Observation 0 tree Direction = -55 ° Image 1. 2. 3. 4. 5.
  29. 30. Context Driven Processing – Earth Observation
  30. 31. Segmentation Result from Street-Model (also centerline)
  31. 32. Segmentation Result from Image – Houses, Trees, Roads and Meadows
  32. 33. Centerlines of Roads Imprinted into Segmentation Result
  33. 34. Conflicts – Streets Run Through Houses
  34. 35. Finding Conflicts Automatically – in Blue
  35. 36. Demo
  36. 37. Different kind of context object
  37. 38. 8:35 Umtata - South Africa 7604 x 4660 pixels
  38. 39. 8:35 Multi-Resolution Segmentation
  39. 40. 10:00 + Merge
  40. 41. 4:30 Multi-Resolution Segmentation on Half Resolution
  41. 42. 3:45 Pixel-Based
  42. 43. Major Roads Within and Outside the City (South Africa)
  43. 44. Major Roads Within and Outside the City (South Africa)
  44. 45. Rural Road Network near Mvezo, South Africa
  45. 46. Munich - 100 MPixels
  46. 47. 32:00
  47. 48. 34:00
  48. 49. 16:00
  49. 50. 3:01 Half Resolution
  50. 51. Different kind of context object
  51. 52. Local Processing
  52. 53. Local Processing
  53. 54. Local Processing
  54. 55. Yield in Case of Dependencies Context objects need to be very reliable Chance for success: 0.95*0.95*0.95*0.95*0.95*0.95 = 0.73 Probabilities multiply
  55. 59. From GIS to GIN (From geographic Information Systems to a gigantic Geographic Information Network)
  56. 60. From GIS to GIN A network of organizations, individuals, and autonomous machines
  57. 61. From GIS to GIN Data provider Government Institution University Satellite Service provider Airplane sensor sensors people people people Drone (Internet) sensor Lidar sensor sensor The Emerging Network Relatively new (in red): New sensors, internet service providers, contribution of private individuals networking of sensors and their data, sensors on people, automated data creation
  58. 62. GIN with Intelligent Processing and Autonomous Machines (IP and AM) Data provider Government Institution University Satellite Airplane sensor sensor sensor sensor people people people sensors Drone Service provider (Internet) IP AM warning Lidar IP IP, AM IP Distributed sensors AM Company, Organization
  59. 63. Autonomous Machines ( AM ) <ul><li>Automatic data creation </li></ul><ul><ul><li>Unmanned vehicles </li></ul></ul><ul><ul><li>satellites, drones </li></ul></ul><ul><ul><li>stationary cameras </li></ul></ul><ul><li>Automatic data analysis </li></ul><ul><ul><li>Definiens- nothing else </li></ul></ul>
  60. 64. Statement: automated data analysis is possible <ul><li>Analysis gets better and better </li></ul><ul><li>Analysis gets simpler and simpler </li></ul><ul><ul><li>Combination of different data </li></ul></ul>
  61. 65. Statement: automated data analysis is possible <ul><li>Analysis gets better and better </li></ul><ul><ul><li>XD </li></ul></ul><ul><li>Analysis gets simpler and simpler </li></ul><ul><ul><li>Combination of different data </li></ul></ul>
  62. 66. Statement: automated data analysis is possible <ul><li>Analysis gets better and better </li></ul><ul><ul><li>XD </li></ul></ul><ul><li>Analysis gets simpler and simpler </li></ul><ul><ul><li>Combination of different data </li></ul></ul>
  63. 67. Example for Simplicity: Combination of Infrared and Lidar Infrared=vegetation
  64. 68. Digital Surface Model (DSM) Lidar: Elevation=vegetation or building
  65. 69. Classification of Aerial Image with DSM (Buildings and Vegetation) Buildings=Elevation-Vegetation
  66. 70. Intelligent automation is possible Consequences? … not only for image analysis
  67. 71. From GIS to GIN (geographic information network) A network of organizations, individuals, and autonomous machines
  68. 72. <ul><li>More data </li></ul><ul><li>New types of data </li></ul><ul><li>More automated data generation </li></ul><ul><li>More private use of geographical information </li></ul><ul><li>Private contribution </li></ul><ul><li>More networking of different data </li></ul><ul><li>More networking of organizations and people </li></ul>The future context for automated image analysis
  69. 73. From GIS to GIN (Geographic Information Network) Data provider Government Institution University Satellite Service provider Airplane sensor sensor people people people Drone (Internet) sensor Lidar sensor sensor
  70. 74. <ul><li>More data </li></ul><ul><li>New types of data </li></ul><ul><li>More automated data generation </li></ul><ul><li>More private use of geographical information </li></ul><ul><li>Private contribution </li></ul><ul><li>More networking of different data </li></ul><ul><li>More networking of organizations and people </li></ul><ul><li>Automated intelligent data processing </li></ul><ul><li>Automated data and event communication </li></ul>The future context for automated image analysis
  71. 75. GIN Plus Intelligent Machines (IM) Data provider Government Institution University Satellite Airplane sensor sensor sensor sensor people people people sensor Drone Service provider (Internet) IM IM warning Lidar
  72. 76. GIN Plus IM Plus Pervasive Computing Data provider Government Institution University Satellite Airplane sensor sensor sensor sensor people people people sensor Drone Lidar Service provider (Internet) IM warning IM
  73. 77. <ul><li>Explosion of data: intelligent data processing is a solution </li></ul><ul><li>More data are turned into information </li></ul><ul><li>Easier access to relevant information </li></ul><ul><li>Explosion of information: context driven data management required </li></ul>Consequence of the consequences

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