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COMPUTER VISION   Creating Artificial Life                                                       Ameer Mohamed Rajah      ...
COMPUTER VISION• Introduction• System of Computer Vision• Applications• Example
WHAT IS COMPUTER VISION?•   Computer vision is a field that includes methods for acquiring,    processing, analyzing, and ...
COMPUTER VISION VS HUMAN VISION     What we see         What a computer sees
COMPUTER VISION VS HUMAN VISION…• Vision is an amazing feat of natural intelligence• More human brain devoted to vision th...
WHY IS COMPUTER VISION DIFFICULT?• It is a many-to-one mapping• It is computationally intensive• We do not understand the ...
RELATION BETWEEN COMPUTER VISION ANDVARIOUS OTHER FIELDS [
COMPUTER VISION SYSTEM METHODS•   Image acquisition•   Pre-processing•   Feature extraction•   Detection/segmentation•   R...
RECOGNITION CUES•   Color•   Texture•   Pattern•   Shape•   Association
MATHEMATICS IN COMPUTER VISION  • Calculus  • Linear Algebra  • Probabilities and Statistics  • Signal Processing  • Proje...
COMPUTER VISION APPLICATIONS•   Controlling processes•   Navigation•   Detecting events•   Organizing information•   Model...
OPTICAL CHARACTER RECOGNITION (OCR)
LOGIN WITHOUT A PASSWORD…
TARGET RECOGNITION
INTERPRETATION OF HIGH RESOLUTIONSATELLITE IMAGES
TRAFFIC MONITORING
FACE DETECTION
3D SHAPE RECONSTRUCTION
OBJECT RECOGNITION (IN MOBILE PHONES)  Google Goggles
SPORTS•   Hawk-Eye
SMART CARS
INTERACTIVE GAMES
INDUSTRIAL ROBOTS    Vision-guided robots position nut runners on wheels
MOBILE ROBOTS
MEDICAL IMAGING
EXAMPLE
CAPTURING DIGITAL PHOTO (SENSOR)
CALCULATE PROBABILITY
PROBABILITY THRESHOLD
CLUMP
FILTER BY SIZE
FOCAL FILTERING FOR SMOOTHING
ERASING OUT
CONVERT TO VECTOR
FILTERING ISLANDS
CLIPPING OUTLIERS
IDENTIFY GEOMETRIC PARAMETERS ANDCALCULATE PARAMETER VALUES
FILTERING BY PROBABILITY
SMOOTHING POLYGONS
CREATE ORIENTED BOUNDING BOX
IDENTIFICATION OF AGRO WELLS
MACHINES CANT REPLICATE HUMAN IMAGE            RECOGNITION           THANK YOU
Computer Vision
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Computer Vision

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Creating Artificial Vision of Artificial Life

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Computer Vision

  1. 1. COMPUTER VISION Creating Artificial Life Ameer Mohamed Rajah GRandD, International Water Management Institute
  2. 2. COMPUTER VISION• Introduction• System of Computer Vision• Applications• Example
  3. 3. WHAT IS COMPUTER VISION?• Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images• Known as Image analysis, Scene Analysis, Image Understanding• duplicate the abilities of human vision by electronically perceiving and understanding an image• Theory for building artificial systems that obtain information from images.• Image data can take many forms, such as a video sequence, depth images, views from multiple cameras, medical scanner, satellite sensors etc.
  4. 4. COMPUTER VISION VS HUMAN VISION What we see What a computer sees
  5. 5. COMPUTER VISION VS HUMAN VISION…• Vision is an amazing feat of natural intelligence• More human brain devoted to vision than anything else• There are about 30,000 visual categories. • Learn 4.5 categories per day  18 years • At age 6, child can learn roughly all 30,000 (13.5day)• Machines Cant Replicate Human Image Recognition, Yet. they do not possess our ability to recognize distorted images
  6. 6. WHY IS COMPUTER VISION DIFFICULT?• It is a many-to-one mapping• It is computationally intensive• We do not understand the recognition problem
  7. 7. RELATION BETWEEN COMPUTER VISION ANDVARIOUS OTHER FIELDS [
  8. 8. COMPUTER VISION SYSTEM METHODS• Image acquisition• Pre-processing• Feature extraction• Detection/segmentation• Recognition an interpretation
  9. 9. RECOGNITION CUES• Color• Texture• Pattern• Shape• Association
  10. 10. MATHEMATICS IN COMPUTER VISION • Calculus • Linear Algebra • Probabilities and Statistics • Signal Processing • Projective Geometry • Computational Geometry • Optimization Theory • Control Theory
  11. 11. COMPUTER VISION APPLICATIONS• Controlling processes• Navigation• Detecting events• Organizing information• Modeling objects or environments• Interaction• Automatic inspection, e.g. in manufacturing applications• much more …...
  12. 12. OPTICAL CHARACTER RECOGNITION (OCR)
  13. 13. LOGIN WITHOUT A PASSWORD…
  14. 14. TARGET RECOGNITION
  15. 15. INTERPRETATION OF HIGH RESOLUTIONSATELLITE IMAGES
  16. 16. TRAFFIC MONITORING
  17. 17. FACE DETECTION
  18. 18. 3D SHAPE RECONSTRUCTION
  19. 19. OBJECT RECOGNITION (IN MOBILE PHONES) Google Goggles
  20. 20. SPORTS• Hawk-Eye
  21. 21. SMART CARS
  22. 22. INTERACTIVE GAMES
  23. 23. INDUSTRIAL ROBOTS Vision-guided robots position nut runners on wheels
  24. 24. MOBILE ROBOTS
  25. 25. MEDICAL IMAGING
  26. 26. EXAMPLE
  27. 27. CAPTURING DIGITAL PHOTO (SENSOR)
  28. 28. CALCULATE PROBABILITY
  29. 29. PROBABILITY THRESHOLD
  30. 30. CLUMP
  31. 31. FILTER BY SIZE
  32. 32. FOCAL FILTERING FOR SMOOTHING
  33. 33. ERASING OUT
  34. 34. CONVERT TO VECTOR
  35. 35. FILTERING ISLANDS
  36. 36. CLIPPING OUTLIERS
  37. 37. IDENTIFY GEOMETRIC PARAMETERS ANDCALCULATE PARAMETER VALUES
  38. 38. FILTERING BY PROBABILITY
  39. 39. SMOOTHING POLYGONS
  40. 40. CREATE ORIENTED BOUNDING BOX
  41. 41. IDENTIFICATION OF AGRO WELLS
  42. 42. MACHINES CANT REPLICATE HUMAN IMAGE RECOGNITION THANK YOU

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