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Introduction to Computer Vision

Introduction to Computer Vision. Lecture 1 Dr. Roger S. Gaborski. Course Goals. Obtain a working knowledge of computer vision Gain an understanding of current research in computer vision Become familiar with programming in the MATLAB environment. Where to Find Me. Office: 70 – 3647

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Introduction to Computer Vision

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  1. Introduction to Computer Vision Lecture 1 Dr. Roger S. Gaborski

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

  3. Where to Find Me • Office: 70 – 3647 • My lab 70-3400 • Office Hours: • Tuesday noon – 2:00pm , in Rm3400 or 3647 • Email: rsg@cs.rit.edu • Homework email: rsg_introcv@gmail.com RS Gaborski

  4. 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 • Topics • Homework • Quizzes and Exams • Projects (4005-757 only) • Grading • Webpage: www.cs.rit.edu/~rsg (includes course calendar on CV page) • Lecture slides will not always be posted on webpage RS Gaborski

  5. 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 TA cannot answer your questions, see me during my office hours • Do not send me email concerning the HW after noon the night before it is due. I will not be able to respond to your email. RS Gaborski

  6. Grader • Jeffrey Zullo • E-Mail: <jlz9811@rit.edu> RS Gaborski

  7. Grading - With Final • Homework 35%(457) 20%(757) • Quizzes/Exams 45% 45% • Final 20% 20% • Project* --- 15% • No Project for 4003-457 • *Project: 757 Individual only, weekly presentation updates RS Gaborski

  8. Grading - Without Final • Homework 35%(457) 20%(757) • Quizzes/Exams 65% 65% • Project* --- 15% • No Project for 4003-457 • *Project: 757 Individual only, weekly presentation updates • Exam and Quiz grade must be at least 80% at the end of week 10 to be excused from final RS Gaborski

  9. Course Grade • 90%-100% A* • 80%-89% B • 70%-79% C • 60%-69% D • <60% F * Note: For example, 89.4 is a ‘B’, 89.5 is rounded to 90 which is an ‘A’ RS Gaborski

  10. Project • Choose from a list of projects provided on course Project Page • Ten minute verbal proposal presentation (see course calendar) • Verbal updates (see course calendar) • *Project grade includes verbal proposal, verbal update reports and final presentation, code and report RS Gaborski

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

  12. 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

  13. Digital Image RS Gaborski

  14. Digital Image RS Gaborski

  15. Digital Image RS Gaborski

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

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

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

  19. 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/

  20. Industrial Applications Non Destructive Testing Inspection / Security

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

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

  23. RS Gaborski

  24. Satellite Images andAstronomy

  25. Satellite Images RS Gaborski www.noaa.gov

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

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

  28. 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 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. 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

  33. RS Gaborski

  34. Student Result RS Gaborski

  35. RS Gaborski

  36. More Categories RS Gaborski

  37. RS Gaborski

  38. How Else Could You Identify Locations? KISS- simpler approach to recognize location that recognizing objects in the image?

  39. iPhoto 09 "Places" Geotagging • http://www.youtube.com/watch?v=GVW8700LrvE RS Gaborski

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

  41. Level of Vision • Low level processing: • Pixel level • Gradient (uniform area, edges) • Intermediate level processing: • Group pixels into line • High level processing • Interpretation of a scene beyond grouping RS Gaborski

  42. Image Models • Task: “Look for an object in an image” • Assume the task is to find rectangle and washer objects RS Gaborski

  43. Image models, continued RS Gaborski

  44. Image Models • Task: “Look for an object in an image” • Assume the task is to find rectangle and washer objects • Find edge pixels and group • Find outlines of objects in the image • Create a model of the object • Rectangle: Four straight lines, Opposite lines equal in length, 90 degree angles, lines connected • Washer: Two concentric circles RS Gaborski

  45. Image models, edges RS Gaborski

  46. Image models, continued One object partially overlaps another RS Gaborski

  47. Objects are 3 Dimensional Rotating Disk Frame 1 Frame 2 Frame 3 RS Gaborski

  48. License Plate Model • Rectangular (depending on viewpoint) • Aspect ratio 2:1 • Textures (characters on license plate) RS Gaborski

  49. RS Gaborski

  50. Face Model http://www.faceresearch.org/ RS Gaborski

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