Creating Artificial Vision of Artificial Life

1. COMPUTER VISION
Creating Artificial Life
Ameer Mohamed Rajah
GRandD, International Water Management Institute

3. COMPUTER VISION
• Introduction
• System of Computer Vision
• Applications
• Example

4. 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.

5. COMPUTER VISION VS HUMAN VISION
What we see What a computer sees

6. 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 Can't Replicate Human Image Recognition, Yet. they
do not possess our ability to recognize distorted images

7. WHY IS COMPUTER VISION DIFFICULT?
• It is a many-to-one mapping
• It is computationally intensive
• We do not understand the recognition problem

8. RELATION BETWEEN COMPUTER VISION AND
VARIOUS OTHER FIELDS [

9. COMPUTER VISION SYSTEM METHODS
• Image acquisition
• Pre-processing
• Feature extraction
• Detection/segmentation
• Recognition an interpretation

10. RECOGNITION CUES
• Color
• Texture
• Pattern
• Shape
• Association

11. MATHEMATICS IN COMPUTER VISION
• Calculus
• Linear Algebra
• Probabilities and Statistics
• Signal Processing
• Projective Geometry
• Computational Geometry
• Optimization Theory
• Control Theory

12. COMPUTER VISION APPLICATIONS
• Controlling processes
• Navigation
• Detecting events
• Organizing information
• Modeling objects or environments
• Interaction
• Automatic inspection, e.g. in manufacturing applications
• much more …...

13. OPTICAL CHARACTER RECOGNITION (OCR)

14. LOGIN WITHOUT A PASSWORD…

15. TARGET RECOGNITION

16. INTERPRETATION OF HIGH RESOLUTION
SATELLITE IMAGES

17. TRAFFIC MONITORING

18. FACE DETECTION

19. 3D SHAPE RECONSTRUCTION

20. OBJECT RECOGNITION (IN MOBILE PHONES)
Google Goggles

21. SPORTS
• Hawk-Eye

22. SMART CARS

23. INTERACTIVE GAMES

24. INDUSTRIAL ROBOTS
Vision-guided robots position nut runners on wheels

25. MOBILE ROBOTS

26. MEDICAL IMAGING

27. EXAMPLE

28. CAPTURING DIGITAL PHOTO (SENSOR)

29. CALCULATE PROBABILITY

30. PROBABILITY THRESHOLD

31. CLUMP

32. FILTER BY SIZE

33. FOCAL FILTERING FOR SMOOTHING

34. ERASING OUT

35. CONVERT TO VECTOR

36. FILTERING ISLANDS

37. CLIPPING OUTLIERS

38. IDENTIFY GEOMETRIC PARAMETERS AND
CALCULATE PARAMETER VALUES

39. FILTERING BY PROBABILITY

40. SMOOTHING POLYGONS

41. CREATE ORIENTED BOUNDING BOX

42. IDENTIFICATION OF AGRO WELLS

43. MACHINES CAN'T REPLICATE HUMAN IMAGE
RECOGNITION
THANK YOU