E Cognition User Summit2009 G Binnig Definiens

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  • 1. Principles of Human Cognition Utilized for Automated Image Analysis Gerd Binnig 3.Nov.2009
  • 2. I think therefore I am
  • 3. Context Driven Image Analysis
    • Context driven through CNL
      • Object oriented
      • Knowledge based
    context ?? YES
  • 4. Which Object ?
  • 5. Which Object ?
  • 6. Context
  • 7. Context
  • 8. Object Oriented and Knowledge Based Thinking We have abstract knowledge about classes of objects and their relations Fork! Knife! Plate between
  • 9. Context Objects – From the Concrete to the Vague Objects 1. CONTEXT Objects 2. CONTEXT Object
  • 10. Context Navigation Image Plate ? ? ? ? Fork Handle Knife Context navigation - most important Handle Blade +1 0 +1 0 0
  • 11. Context Navigation Context slowly builds up The easy and well defined “objects” first Image Plate ? ? ? ? Fork Handle Knife Handle
  • 12. Only vague objects ?
  • 13. Network of Objects The objects are undefined The relations are concrete
  • 14. Cognition Network Language - CNL
    • Context driven
      • Knowledge based
      • Object based
      • Local
    • Script based
  • 15.
        • Process Hierarchy
        • programming by multiple choice
        • .
        • Knowledge Hierarchy
        • natural formulation
        • .
        • Navigation
        • for directed local processing
        • .
        • Object Hierarchy
        • evolves in the course of processing
    The Elements of CNL MRS, Chessboard, Merge, Classification, .. Fork! Knife! Plate between
  • 16.
    • Context driven
      • Knowledge based
      • Object based
      • Object-Pixel unification
      • Local
    • Script based
    • Multi-Image capability
    • Up to 4 Dimensions
    The Elements of CNL – Version 8
  • 17.
        • Process Hierarchy
        • programming by multiple choice
        • .
        • Knowledge Hierarchy
        • natural formulation
        • .
        • Navigation
        • for directed local processing
        • .
        • Object Hierarchy
        • evolves in the course of processing
    The Elements of CNL – V8 MRS, Chessboard, Merge, Classification, .. Region O + P
  • 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
      • m=meter
    Traveling through the Dimensions of Space 100
  • 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. Biopsy Tissue Clinical Applications Serum Cells X-Ray Mammography CT Organs MRI low res. Organs CT Head/Neck MRI Ventricles CT Lymph Nodes
  • 21. Context Driven Processing – Cell Cultures -1 +1 3. Micro- Nucleus Context Objects : Nuclei Cells 1. Nuclei 2. Cells Image
  • 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. Spinal Cord +1 Context Driven Processing – CT Spine Context Objects : Spinal cord Liver Kidney Spleen Spine
  • 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
  • 27. Screenshots from Internet (Google)
  • 28. Hannover – University – Detecting HOUSES What is easy and well defined? ..and can be used as context?
  • 29. Meadow Potential shadow of tree 0 Context Driven Processing – Earth Observation 0 tree Direction = -55 ° Image 1. 2. 3. 4. 5.
  • 30. Context Driven Processing – Earth Observation
  • 31. Segmentation Result from Street-Model (also centerline)
  • 32. Segmentation Result from Image – Houses, Trees, Roads and Meadows
  • 33. Centerlines of Roads Imprinted into Segmentation Result
  • 34. Conflicts – Streets Run Through Houses
  • 35. Finding Conflicts Automatically – in Blue
  • 36. Demo
  • 37. Different kind of context object
  • 38. 8:35 Umtata - South Africa 7604 x 4660 pixels
  • 39. 8:35 Multi-Resolution Segmentation
  • 40. 10:00 + Merge
  • 41. 4:30 Multi-Resolution Segmentation on Half Resolution
  • 42. 3:45 Pixel-Based
  • 43. Major Roads Within and Outside the City (South Africa)
  • 44. Major Roads Within and Outside the City (South Africa)
  • 45. Rural Road Network near Mvezo, South Africa
  • 46. Munich - 100 MPixels
  • 47. 32:00
  • 48. 34:00
  • 49. 16:00
  • 50. 3:01 Half Resolution
  • 51. Different kind of context object
  • 52. Local Processing
  • 53. Local Processing
  • 54. Local Processing
  • 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
  • 56.  
  • 57.  
  • 58.  
  • 59. From GIS to GIN (From geographic Information Systems to a gigantic Geographic Information Network)
  • 60. From GIS to GIN A network of organizations, individuals, and autonomous machines
  • 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
  • 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
  • 63. Autonomous Machines ( AM )
    • Automatic data creation
      • Unmanned vehicles
      • satellites, drones
      • stationary cameras
    • Automatic data analysis
      • Definiens- nothing else
  • 64. Statement: automated data analysis is possible
    • Analysis gets better and better
    • Analysis gets simpler and simpler
      • Combination of different data
  • 65. Statement: automated data analysis is possible
    • Analysis gets better and better
      • XD
    • Analysis gets simpler and simpler
      • Combination of different data
  • 66. Statement: automated data analysis is possible
    • Analysis gets better and better
      • XD
    • Analysis gets simpler and simpler
      • Combination of different data
  • 67. Example for Simplicity: Combination of Infrared and Lidar Infrared=vegetation
  • 68. Digital Surface Model (DSM) Lidar: Elevation=vegetation or building
  • 69. Classification of Aerial Image with DSM (Buildings and Vegetation) Buildings=Elevation-Vegetation
  • 70. Intelligent automation is possible Consequences? … not only for image analysis
  • 71. From GIS to GIN (geographic information network) A network of organizations, individuals, and autonomous machines
  • 72.
    • More data
    • New types of data
    • More automated data generation
    • More private use of geographical information
    • Private contribution
    • More networking of different data
    • More networking of organizations and people
    The future context for automated image analysis
  • 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
  • 74.
    • More data
    • New types of data
    • More automated data generation
    • More private use of geographical information
    • Private contribution
    • More networking of different data
    • More networking of organizations and people
    • Automated intelligent data processing
    • Automated data and event communication
    The future context for automated image analysis
  • 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
  • 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
  • 77.
    • Explosion of data: intelligent data processing is a solution
    • More data are turned into information
    • Easier access to relevant information
    • Explosion of information: context driven data management required
    Consequence of the consequences