Lecture No. 1 introduction.pptx
- 1. 1 Computer Imaging Digital Image Processing Computer Vision Introduction “One picture is worth more than ten thousand words”
- 2. 2 Computer Imaging Image analysis involves examination of image data to facilitate solving an imaging problem A computer vision application can be considered to be a deployed image analysis system In a computer vision system the images are intended to be “seen” by a computer
- 3. 3 Image Processing Computer Vision Input Image Output Image (for human consumption) Topics Image restoration Image enhancement Image compression Image morphing, etc. Input Image Output Image attributes (for computer use) Topics Image segmentation Image recognition (feature extraction) Motion analysis & tracking 3D shape reconstruction, etc. Computer Imaging Computer Imaging can be separated into two different but overlapping areas:
- 4. 4 Digital images: A pixel has a location (the spatial coordinate) and a value. Digital images can be from many types of sources. Digital image processing Digital image processing is closely related to signal processing at one end, and to computer vision at the other end. Digital Image Processing (DIP)
- 5. 5 Computer vision is concerned with making computers “understand” what is contained in an image and other functions of human vision. It is sometimes considered to be a topic of artificial intelligence. However, it is not possible to state exactly where image processing ends and computer vision begins. If you compare a digital image processing book and a computer vision book, you will find a lot of overlap. Computer Vision
- 7. 7 Why DIP? Hospitals (Medical Imaging: Radiology - MRI, CT Scan, X-Rays, Ultrasound, Retinopathy and other Pathologies) Forensic Departments (like Punjab Forensic Science Agency: Firearms identification, lab. tests, Image & video forensics) Software Houses (Games developments including Kinect, Touch applications) Electronic & Print Media (Artistic effects, Morphology) Security Agencies, Industries & Offices (Biometrics, Surveillance, Object & Speed recognition) Pakistan Air Force & NESCOM (UAV: drone project with NESCOM) GIS (SUPARCO: Satellite Imagery; Terrain Classification; Meteorology; Weather) OCR (text, number plate recognitions etc.) MRD (Multidisciplinary Research & Development)
- 8. 8 Defect Detection in Microdisplay Chips Captured image After defect detection After blob analysis Inspection speed vastly increased Much lower error rates More complete analysis Cost savings
- 9. 9 Image Processing Computer imaging application involving a human being in the visual loop Consists of following major fields: o Image enhancement o Image restoration o Image compression
- 10. 10 Distorted image Restored image Geometric distorted image Restored image Image Restoration
- 11. 11 Image Enhancement Poor contrast image Enhanced image Original image Sharpened image
- 12. 12 Restored image Noisy image Image Restoration
- 13. 13 13 Image Compression Original image Compressed image(1/10) Compressed image(1/20) Compressed image(1/30)
- 14. 14 1. Computer Aided Inspection of Grains 2. Capturing outlines of planar images 3. Background adjustment of a digital photograph 4. Area of an irregular image 5. Traffic signal alert 6. Safe drive alert 7. Transformation and restoration of B &W photograph into fine & colored picture 8. Enhancement of satellite images taken in cloudy area 9. Computer Aided Automatic Grading of Physical Models 10. Image & video compression 11. Image audio transformation system for blind people 12. Diabetes retinopathy on digital fundus images 13. Firearm identification Industrial & Research Projects
- 15. 15 MRI brain images classification Classification of brain MRI images using LDyWT (Lifting Dyadic Wavelet Transform), Genetic algorithms, and SVM. Denoising Medical Images To investigate for a denoising method for medical images using dyadic wavelet transform (DyWT) and one-dimensional singularity function model. Blood Vessel Segmentation in Retinal Images To investigate for blood vessel segmentation method in retinal images using A Generative Model for Image Segmentation Based on Label Fusion Industrial & Research Projects
- 16. 16 Every picture tells a story Can a computer infer what happened from the image?
- 17. 17 The goal of Computer Imaging
- 18. 18 Can computers match (or beat) human vision? Yes and no (but mostly no!) humans are much better at “hard” things computers can be better at “easy” things
- 19. 19 Why Study Computer Imaging? Millions of images being captured all the time Lots of useful applications The next slides show the current state of the art
- 20. 20 Optical character recognition (OCR) Digit recognition, AT&T labs http://www.research.att.com/~yann/ Technology to convert scanned docs to text • If you have a scanner, it probably came with OCR software License plate readers http://en.wikipedia.org/wiki/Automatic_number_plate_recognition
- 21. 21 Face detection Many new digital cameras now detect faces Canon, Sony, Fuji, …
- 22. 22 Smile detection? Sony Cyber-shot® T70 Digital Still Camera
- 23. 23 Face recognition Who is she? Sharbat Gula at age 12 in an Afgan refugee camp in 1984 Traced in 2002 but is she the same person?
- 24. 24 Vision-based biometrics “How the Afghan Girl was Identified by Her Iris Patterns” Read the story 1984 2002
- 25. 25 Login without a password… Fingerprint scanners on many new laptops, other devices Face recognition systems now beginning to appear more widely http://www.sensiblevision.com/
- 26. 26 Object recognition (in mobile phones) This is becoming real: Microsoft Research Point & Find, Nokia
- 27. 27 Earth viewers (3D modeling) Image from Microsoft’s Virtual Earth (see also: Google Earth)
- 28. 28 Phototourism Automatic 3D reconstruction from Internet photo collections “Statue of Liberty” 3D model Flickr photos “Half Dome, Yosemite” “Colosseum, Rome”
- 29. 30 Smart cars Mobileye Vision systems currently in high-end BMW, GM, Volvo models By 2010: 70% of car manufacturers.
- 30. 31 Vision-based interaction (and games) Nintendo Wii has camera-based IR tracking built in. See Lee’s work at CMU on clever tricks on using it to create a multi-touch display! Digimask: put your face on a 3D avatar. “Game turns moviegoers into Human Joysticks”, CNET Camera tracking a crowd, based on this work.
- 31. 32 Vision in space Vision systems (JPL) used for several tasks • Panorama stitching • 3D terrain modeling • Obstacle detection, position tracking • For more, read “Computer Vision on Mars” by Matthies et al. NASA'S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007.
- 32. 33 Robotics http://www.robocup.org/ NASA’s Mars Spirit Rover http://en.wikipedia.org/wiki/Spirit_rover
- 33. 34 Medical imaging Image guided surgery Grimson et al., MIT 3D imaging MRI, CT
- 34. 35 Current state of the art You just saw examples of current systems. Many of these are less than 5 years old This is a very active research area, and rapidly changing Many new apps in the next 5 years To learn more about imaging applications and companies David Lowe maintains an excellent overview of vision companies http://www.cs.ubc.ca/spider/lowe/vision.html
- 35. 36 Objectives To familiarize you with the basic techniques and terminology of Computer Imaging To provide the sufficient background for FYPs on computer imaging To ready you for learning the advanced topics of Computer Imaging course at post graduate level, like wavelets, camera models and calibration, motion analysis and tracking, video understanding, image mosaics, 3D-shape reconstruction, etc. To excite & stimulate you!