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Introduction to Computer Vision Location 70- 3455

Introduction to Computer Vision Location 70- 3455. Lecture 1 Dr. Roger S. Gaborski. Where to Find Me. Office: 70 – 3647 Office Hours: Tuesday 10am-noon, in Rm3400 or 3647 My lab 70-3400 Email: rsg@cs.rit.edu Homework email: rsg_introcv@gmail.com. Assistant. Yuheng ‘Helen’ Wang

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Introduction to Computer Vision Location 70- 3455

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  1. Introduction to Computer Vision Location 70-3455 Lecture 1 Dr. Roger S. Gaborski

  2. Where to Find Me • Office: 70 – 3647 • Office Hours: • Tuesday 10am-noon, in Rm3400 or 3647 • My lab 70-3400 • Email: rsg@cs.rit.edu • Homework email: rsg_introcv@gmail.com RS Gaborski

  3. Assistant • Yuheng ‘Helen’ Wang • Office Hours: TBD RS Gaborski

  4. Grader • Jeffrey Zullo • E-Mail: <jlz9811@rit.edu> 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 • Topics • Homework • Quizzes and Final • 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

  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 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

  7. Grading • Homework 35%(457) 20%(757) • Quizzes/Exams 65% 65% • Project* --- 15% • No Project for 4003-457 • *Project: 757 Individual only, weekly presentation updates RS Gaborski

  8. 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

  9. 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

  10. Computer Vision • Low-level Processes • Primitive operations • Reduce noise • Enhance contrast • Sharpen image • Mid-level Processes • Input are images, output are attributes extracted from image (edges, contours and identity of objects • Segmentation (partition image into objects or regions) • Description of objects/regions RS Gaborski

  11. Computer Vision • High-level Processes • Understanding content of images RS Gaborski

  12. SUMMARY:Goals of Computer Vision • Image Enhancement • Reduce noise in an image thereby revealing features in the image, extract features • Image Processing Operations • Segment the image into objects • Label individual objects • Image Understanding • Understand the ‘content’ of an image or sequence of images (video) • Extract meaning of the image RS Gaborski

  13. 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

  14. Digital Image RS Gaborski

  15. Digital Image RS Gaborski

  16. Digital Image RS Gaborski

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

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

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

  20. 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/

  21. Industrial Applications Non Destructive Testing Inspection / Security

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

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

  24. RS Gaborski

  25. Satellite Images andAstronomy

  26. Satellite Images RS Gaborski www.noaa.gov

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

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

  29. 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

  30. RS Gaborski

  31. Student Result RS Gaborski

  32. RS Gaborski

  33. RS Gaborski

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

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

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

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

  38. Image models, continued RS Gaborski

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

  40. Image models, edges RS Gaborski

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

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

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

  44. RS Gaborski

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

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

  47. Face Model Features: eyes, nose, mouth, shape of face (oval) Spatial orientation of features Issues to investigate: how do we detect features? Normalize for different faces? Scale? Orientation? Cluttered background? RS Gaborski

  48. iPhoto 09 "Faces" Face Recognition, http://www.youtube.com/watch?v=NzCV_L87J2I • Digital Face Recognition, http://www.youtube.com/watch?v=obyPvoSTo-o&feature=related RS Gaborski

  49. Deformable Objects in Video RS Gaborski

  50. Finding Cars in ImagesTraining RS Gaborski

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