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CSCI-631 Foundations of Computer Vision

CSCI-631 Foundations of Computer Vision. Lecture 1 Dr. Roger S. Gaborski. Course Goals. Obtain a working knowledge of computer vision Become familiar with programming in the MATLAB environment Gain an understanding of current research in computer vision

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CSCI-631 Foundations of Computer Vision

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  1. CSCI-631Foundations of Computer Vision Lecture 1 Dr. Roger S. Gaborski

  2. Course Goals • Obtain a working knowledge of computer vision • Become familiar with programming in the MATLAB environment • Gain an understanding of current research in computer vision • Demonstrate computer vision knowledge by computing a computer vision research project RS Gaborski

  3. Teaching Assistant • Zhen Kuang He • E-Mail: zxh3909@rit.edu • Responsible for homework grading and tutoring • Tutoring hours in lab (70-3400): • TBD RS Gaborski

  4. Where to Find Me • Office: 70 – 3647 • My lab 70-3400 • Office Hours: • Tuesday and Thursday, 11am-noon in my lab or office • My appointment • email: rsg@cs.rit.edu • HOMEWORK EMAIL: rsg.CV631@gmail.com RS Gaborski

  5. Course Outline • Textbook – Digital Image Processing using MATLAB • SECOND EDITION 2009 Gatesmark Publishing • Online MATLAB tutorial-Register at Mathworks: • http://www.mathworks.com/academia/student_center/tutorials/launchpad.html • MATLAB tutorial: http://etools.fernuni.ch/matlab/matlab1/en/html/startpage.html • Homework – individual and team • Quizzes, Exams and Final • Team Project • Attendance Required • Grading • Webpage: www.cs.rit.edu/~rsg (includes course calendar on CV page) • Lecture slides will not always be posted on webpage RS Gaborski

  6. Homework • Questions concerning Homework • Do not wait until the night before its due to start working on the HW • Ask questions in class concerning HW • First, ask the TA during his office hours • If the TA cannot answer your questions, see me during my office hours • Do not send me email concerning the HW afternoon the night before it is due. I will not be able to respond to your email. RS Gaborski

  7. Grading • Homework (Individual and Team) 20% • Quizzes/Exams/Final 50% • Team Project 20% • Attendance (Required) 10% RS Gaborski

  8. Typical Course Grade • 90%-100% A* • 80%-89% B • 70%-79% C • 60%-65% D • <65% F * Note: For example, 89.4 is a ‘B’, 89.5 is rounded to 90 which is an ‘A’ RS Gaborski

  9. What has changed in the computer vision field in the last 5 to 10 years? RS Gaborski

  10. Images are Everywhere • On the web – flickr, Google Images, YouTube • On your computer – iPhoto, Picasa • Video Surveillance: • Streets • Hotels • Businesses • Parking lots RS Gaborski

  11. Computer Vision – Interpretation of Images • Digital photographs • Medical radiographic images • Functional magnetic resonance imaging (fMRI) • Medical ultrasound • Industrial radiographic images • Digital video images • Satellite images • Astronomy RS Gaborski

  12. Digital Image RS Gaborski

  13. Digital Image RS Gaborski

  14. Digital Image RS Gaborski

  15. Medical Related Images Information obtained from images: Bone structure Soft Tissue Brain Activity

  16. Medical Radiographic Image www.4umi.com/image/x-ray.jpg RS Gaborski

  17. Medical Ultrasound http://keystone.stanford.edu/~huster/photos/i/ultrasound.640.jpg RS Gaborski

  18. Functional MRI A 20-year old female drinker A 20-year old female nondrinker Response to the spatial working memory task. Brain activation is shown in bright colors. RS Gaborski www.alcoholism2.com/

  19. Industrial Applications Non Destructive Testing Inspection / Security

  20. Industrial Radiographic Image www.vidisco.com/ CabinetXrayMic80A_01.htm RS Gaborski

  21. Industrial Radiographic Image Pseudo- color www.vidisco.com/ CabinetXrayMic80A_01.htm RS Gaborski

  22. RS Gaborski

  23. Satellite Images andAstronomy

  24. Satellite Images RS Gaborski www.noaa.gov

  25. Astronomy Images www.sdsc.edu/ sciencegroup/astronomy/ RS Gaborski

  26. Astronomy Images astro.martianbachelor.com/ RS Gaborski

  27. Video Enhancement • Invisible motion in video • Prof. William T. Freeman • http://www.youtube.com/watch?v=sVlC_-e-4yg RS Gaborski

  28. How Hard Is It To Find Objects in an Image?A Few Observations • Object recognition is a very difficult problem • Objects can be rigid, or flexible • Finding a specific object ( is easier than finding all objects that belong to a category RS Gaborski

  29. Find a Yellow 911 Porsche RS Gaborski

  30. Find All Cars in an Image RS Gaborski

  31. What About Background IssuesSeparating the car from the background RS Gaborski

  32. How many pills in this image? http://i.telegraph.co.uk/multimedia/archive/01384/pills_1384371c.jpg RS Gaborski

  33. Image Database Problem • Assume you have taken pictures with your digital camera the last three years • You now have 4000 pictures stored on your computer’s hard drive • How do you sort them? RS Gaborski

  34. Potential categories: 1. Road 2. Field 3. Beach 4. Residential 5. Forest 6. Mountain RS Gaborski

  35. Streets RS Gaborski

  36. Open Country RS Gaborski

  37. Student Result RS Gaborski

  38. RS Gaborski

  39. More Categories RS Gaborski

  40. How Else Could You Identify Locations? Recognize objects in the image?

  41. How do you find a particular face • How do you find a particular object in an image? • Faces • Cars • Buildings • etc RS Gaborski

  42. Images are simply represented by numbers (pixels) Values = 0 Values = 255

  43. [ 0, 64, 128, 192, 255] RS Gaborski

  44. RS Gaborski

  45. Grayscale version of image RS Gaborski

  46. Small region of image Region: rows 6 to 16, columns 18 to 28) 52 53 53 54 54 64 170 186 180 178 174 54 54 53 49 70 144 186 181 180 177 175 52 53 49 77 166 184 172 170 170 172 172 52 48 87 174 186 175 172 172 171 168 164 46 90 185 189 180 175 171 170 168 164 164 67 177 185 173 173 172 173 171 168 164 167 145 187 173 172 170 168 167 169 170 169 166 189 179 174 170 171 171 169 168 168 170 170 173 173 176 175 171 170 171 169 168 168 169 174 172 171 175 177 172 170 170 170 170 169 175 174 172 172 171 174 172 169 170 171 170 RS Gaborski

  47. Brightness Mapped to Color RS Gaborski

  48. Brightness Mapped to Height RS Gaborski

  49. RS Gaborski

  50. Absolute Value of Difference of Adjacent Horizontal Values 1 0 1 0 10 106 16 6 2 4 0 1 4 21 74 42 5 1 3 2 1 4 28 89 18 12 2 0 2 0 4 39 87 12 11 3 0 1 3 4 44 95 4 9 5 4 1 2 4 0 110 8 12 0 1 1 2 3 4 3 42 14 1 2 2 1 2 1 1 3 10 5 4 1 0 2 1 0 2 0 0 3 1 4 1 1 2 1 0 1 2 1 4 2 5 2 0 0 0 1 1 2 0 1 3 2 3 1 1 1 RS Gaborski

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