Computer Vision
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Computer vision is a field that uses techniques to electronically perceive and understand images. It involves acquiring, processing, analyzing and understanding images and can take forms like video sequences. Computer vision aims to duplicate human vision abilities through artificial systems. It has applications in areas like manufacturing inspection, medical imaging, robotics, traffic monitoring and more. Some techniques used in computer vision include image acquisition, preprocessing, feature extraction, detection, recognition and interpretation.
Computer Vision
- 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…
- 16. INTERPRETATION OF HIGH RESOLUTION SATELLITE IMAGES
- 18. FACE DETECTION
- 20. OBJECT RECOGNITION (IN MOBILE PHONES) Google Goggles
- 21. SPORTS • Hawk-Eye
- 22. SMART CARS
- 24. INDUSTRIAL ROBOTS Vision-guided robots position nut runners on wheels
- 25. MOBILE ROBOTS
- 26. MEDICAL IMAGING
- 27. EXAMPLE
- 28. CAPTURING DIGITAL PHOTO (SENSOR)
- 31. CLUMP
- 32. FILTER BY SIZE
- 33. FOCAL FILTERING FOR SMOOTHING
- 34. ERASING OUT
- 38. IDENTIFY GEOMETRIC PARAMETERS AND CALCULATE PARAMETER VALUES
- 41. CREATE ORIENTED BOUNDING BOX
- 42. IDENTIFICATION OF AGRO WELLS
- 43. MACHINES CAN'T REPLICATE HUMAN IMAGE RECOGNITION THANK YOU