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Image Processing and Computer Vision

Image Processing and Computer Vision. Lecture 4, Multimedia E-Commerce Course November 5, 2002 Mike Christel (significant input by Henry Schneiderman, http://www.cs.cmu.edu/~hws). Carnegie Mellon. Copyright 2002 Michael G. Christel and Alexander G. Hauptmann. Outline.

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Image Processing and Computer Vision

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  1. Image Processing and Computer Vision Lecture 4, Multimedia E-Commerce Course November 5, 2002 Mike Christel (significant input by Henry Schneiderman, http://www.cs.cmu.edu/~hws) CarnegieMellon • Copyright 2002 Michael G. Christel and Alexander G. Hauptmann

  2. Outline • Defining Image Processing and Computer Vision • Emerging Technology • Digitization of documents • Digitization of images/photographs • Biometrics • Management of images on computers • Other: manufacturing, military, games, … • Research in Image Processing and Computer Vision • Automatically Finding Faces and Cars • Content-based Image Retrieval

  3. image image Image Processing vs. Computer Vision • Image Processing • Research area within electrical engineering/signal processing • Focus on syntax, low level features • Computer Vision • Research area within computer science/artificial intelligence • Focus on semantics, symbolic or geometric descriptions Faces People Chairs etc. image

  4. Optical Character Recognition (OCR) • First patent in OCR in 19th century • First applications in post-office and banks • Documents easier to distribute, search, organize, and edit in digital form • Typewriter has been replaced by word processor • Lots of legacy materials (the world’s libraries of books) available only in print • State of the art not perfect, but 99% accurate on cleanly printed pages • Examples of errors. . .

  5. Heavy Print Output from 3 commercial OCR systems

  6. Light Print

  7. Stray Marks

  8. Typography

  9. Processing Overlaid Text in Video Text Area Detection Video The Video OCR (VOCR) process used by the Informedia research group at Carnegie Mellon Text Area Preprocessing Commercial OCR ASCII Text

  10. Text Area Detection

  11. Video FramesFiltered Frames AND-ed Frames (1/2 s intervals) CarnegieMellon • Copyright 2002 Michael G. Christel and Alexander G. Hauptmann

  12. VOCR Preprocessing Problems

  13. Augmenting VOCR with Dictionary Look-up

  14. Handwriting Recognition • Natural progression to OCR work for print • Works if constraints on writer, e.g. palm pilot, where user is asked to conform to specific style or convention

  15. Other Document Processing • Not just for text. . . • Examples: • Engineering document to CAD file • Maps to GIS format • Music score to MIDI representation

  16. Outline • Defining Image Processing and Computer Vision • Emerging Technology • Digitization of documents • Digitization of images/photographs • Biometrics • Management of images on computers • Other: manufacturing, military, games, … • Research in Image Processing and Computer Vision • Automatically Finding Faces and Cars • Content-based Image Retrieval

  17. Digital Cameras = Convenience • Easy to capture photos • Easy to store and organize photos • Easy to duplicate photos • Easy to edit photos • Rough Multimedia eCommerce class survey: • 1999: 10% own digital cameras • 2000: 25% • 2001: 50% • 2002: ??

  18. Digital Camera Cautions Via “Photo Industry Reporter” e-Magazine at: http://www.photoreporter.com/2002/10-21/photokina_report_look_at_35mm.html • Film cameras still outsell digital cameras by almost three to one • The household penetration of digital is at about 15% • “But let’s face it: film’s days are numbered. Anyone staying solely with film these days will have a glorious buggy whip in a market that will be clamoring for cars.”

  19. Digital Camera Growth • Photo Marketing Association on US digital camera sales: • 4.5 million in 2000 • 6.9 million in 2001 • Projected 9.3 million for 2002 • http://www.visioneer.com/About/press/june2402.html • InfoTrends Research Group estimates that the U.S. photo-enabled TV set-top installed base will grow from less than 1 million units in 2002, to over 114 million units in 2006. Household penetration will climb from under 1% to around 85%. • InfoTrends projects digital camera sales to grow at a rate of 38% through 2003

  20. State of the Art: Digital Cameras • Film is currently better in resolution and color • Professional photographers • Digital for low quality newspaper advertisements • Film for portrait photos • Computer storage limitations: 1 high resolution digital image = 20-25 Megabytes • http://pic.templetons.com/brad/photo/pixels.html • 3500 line pairs/35 mm or about 5000 dots/inch, but grainy • At 3:2 frame size, ~20 million pixels • Conclusion: “a 5300 x 4000 digital camera would produce a shot equivalent to a scan from a quality 35mm camera -- provided you can get more than 8 bits per pixel. …A 3000 x 2000 digital camera would match the 35mm for a good percentage of shots.” • Printing: home printers not comparable to commercial printers

  21. Future of Digital Cameras • Improved resolution and color • “Smart” cameras • More programmable features • Auto-focus on object of interest • “Everything in focus” photo • Capture photo when event X occurs

  22. Outline • Defining Image Processing and Computer Vision • Emerging Technology • Digitization of documents • Digitization of images/photographs • Biometrics • Management of images on computers • Other: manufacturing, military, games, … • Research in Image Processing and Computer Vision • Automatically Finding Faces and Cars • Content-based Image Retrieval

  23. Biometrics • Technology for identification • Finger/palm print • Iris • Face

  24. Fingerprints • Minutae – spits and merges of ridges

  25. Face Identification • Not quite reliable yet. • Performance degrades rapidly with uncontrolled lighting, facial expression, and size of database • Several companies exist: • Visionics (Rockfeller University spin-off) • Viisage (MIT spin-off) • EyeMatic (USC spin-off) • Miros (MIT spin-off) • Banque-Tec Intl (Australia) • C-VIS Computer Vision (Germany) • LAU Technologies • Commercial systems installed in London and Brazil to catch criminals

  26. Original Image (1962) Computer-Aged (1997) Actual Photo (1997) Automatic Age Progression

  27. Outline • Defining Image Processing and Computer Vision • Emerging Technology • Digitization of documents • Digitization of images/photographs • Biometrics • Management of images on computers • Other: manufacturing, military, games, … • Research in Image Processing and Computer Vision • Automatically Finding Faces and Cars • Content-based Image Retrieval

  28. Management of images on computers • Compression – reducing storage size needed for images • Watermarking – Protecting copyright • Microsoft, Bell Labs, NEC, etc. Visible watermark

  29. Photo Manipulation • Adobe Photoshop, Corel PhotoPaint, Pixami, PhotoIQ, etc. • Image editing: crop an image, adjust the color, paint over part of any image, airbrush part of an image, combine images, etc. • Future: Applications of computer vision, e.g., discriminating foreground from background.

  30. Online Digital Image Collections • Stock photos of use to graphic designers, artists, etc. • Large collections of images exist • Corbis 67 million images • Getty 70 million stock photography images • AP collects 1000s of digitized images per day

  31. Outline • Defining Image Processing and Computer Vision • Emerging Technology • Digitization of documents • Digitization of images/photographs • Biometrics • Management of images on computers • Other: manufacturing, military, games, … • Research in Image Processing and Computer Vision • Automatically Finding Faces and Cars • Content-based Image Retrieval

  32. Inspection for Manufacturing • Occum – inspection of printed circuit boards ($100M / year) • Cognex – Do-it-yourself toolkits for inspection (400 employees)

  33. Automatic Target Recognition (ATR) • Finding mines, tanks, etc. • Billion dollar a year industry • Martin-Lockheed, TSR, Northrup-Grumman, other aerospace contractors. • Various types of imagery: • Synthetic Aperture Radar (SAR), Sonar, hyper-spectral imagery (more than 3 colors)

  34. Aerial Photo Interpretation • Also referred to as “automated cartography” • Classification of land-use: forest, vegetation, water • Identification of man-made objects: buildings, roads, etc.

  35. Better Security Cameras • Cameras that are responsive to the environment • Track and zoom on moving objects • Automatic adjustment of contrast

  36. Medical imagery • Medical image libraries for study and diagnosis • Image overlay to guide surgeons

  37. History • 1980’s ~100 companies – manufacturing applications mostly • Early 1990’s less than 10 companies • Late 1990’s ~100 companies – face recognition, intelligent teleconferencing, inspection, digital libraries, medical imaging

  38. Outline • Defining Image Processing and Computer Vision • Emerging Technology • Digitization of documents • Digitization of images/photographs • Biometrics • Management of images on computers • Other: manufacturing, military, games, … • Research in Image Processing and Computer Vision • Automatically Finding Faces and Cars • Content-based Image Retrieval

  39. Image Processing: Filtering Enhancing an image’s quality for human viewing, e.g., in medical imaging or in telescopic views of space

  40. Image Processing: Compression • Lossless – No loss in quality: gif, tiff • Lossy – Original image cannot be reconstructed: jpeg • New work on advancing lossy compression strategies with fewer visual artifacts: JPEG 2000 and wavelet transformations

  41. Image Processing: Watermarking • Information hiding • Protecting copyright

  42. Image Processing: Transformation • Transforming image can make it easier to analyze Wavelet transform of image

  43. Wavelet Coefficients Horizontal LP, Vertical LP Horizontal LP, Vertical HP Horizontal HP, Vertical LP Horizontal HP, Vertical HP

  44. 5/3 Linear Phase Wavelets Linear phase 5/3: c[n] = {-1, 2,6,2,-1}, d[n]={1,-2,1} g[n] = {1, 2,-6,2, 1}, f[n]={1, 2,1}

  45. Computer Vision: 3D Shape Reconstruction • Use images to build 3D model of object or site 3D site model built from laser range scans collected by CMU autonomous helicopter

  46. Computer Vision: Guiding Motion • Visually guided manipulation • Hand-eye coordination • Visually guided locomotion • robotic vehicles CMU NavLab II

  47. Computer Vision: Recognition & Classification

  48. Challenges in Object Recognition 245 267 234 142 22 28 38 121 156 187 98 73 32 12 123 21 21 38 209 237 121 99 87 59 197 216 244

  49. LargeQuantityofData Segmentationand HierarchicalAnalysis Robust Algorithms Lips Face Intra-class Object Variation Large number of Object Classes Hand Gesture Text License Plate Clock Vehicle Building Automated Learning Advanced Image Enhancement Low Image Quality Object Recognition Research Object Detection Quality/Quantity Issues Object Detection Issues

  50. Intra-Class Variation

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