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Introduction to Computer Vision. Lecture 1 Dr. Roger S. Gaborski. Where to Find Me. Office: 70 – 3647 Office Hours: Tuesday  3:00 - 4:00pm (I will be in either  my office or my lab, 70-3400) Thursday 2:00 - 3:00pm (I will be either in my lab 70-3400 or my office)

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Introduction to Computer Vision


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

    2. Where to Find Me • Office: 70 – 3647 • Office Hours: • Tuesday  3:00 - 4:00pm (I will be in either  my office or my lab, 70-3400) • Thursday 2:00 - 3:00pm (I will be either in my lab 70-3400 or my office) • Other times by appointment (No appointments on Mondays and Fridays) • Often in my lab or office Wednesdays after 11:00am RS Gaborski

    3. Goals of Computer Vision • Image Enhancement • Reduce noise in an image thereby revealing features in the image • 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

    4. Course Outline • Optional Textbook • Online MATLAB tutorial • Topics • Homework • Exams • Projects (4005-757 only) • Grading • Webpage: www.cs.rit.edu/~rsg (includes course calendar on CV page) RS Gaborski

    5. Grading (with Final) • Homework 30%(457) 20%(757) • Quizzes 50% 50% • Project* --- 10% • Final 20% 20% • No Project for 4003-457 • *Project: 757 Individual only, also, presentation RS Gaborski

    6. Grading (without Final) • 4003-457 • Homework 40% • Quizzes 60% • 4005-757 • Homework 30% • Quizzes 60% • Project 10% RS Gaborski

    7. 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

    8. Project • Choose from a list of projects provided on course Project Page • Lecture 10 – One page Project Proposal on your webpage* • Weekly updates* starting with Lecture 11 – see course calendar • *Project grading includes proposal and weekly update progress RS Gaborski

    9. 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

    10. Digital Image RS Gaborski

    11. Digital Image RS Gaborski

    12. Digital Image RS Gaborski

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

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

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

    16. 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/

    17. Industrial Applications Non Destructive Testing Inspection / Security

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

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

    20. RS Gaborski

    21. Satellite Images andAstronomy

    22. Satellite Images RS Gaborski www.noaa.gov

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

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

    25. 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

    26. Sample Images RS Gaborski

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

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

    29. Image models, continued RS Gaborski

    30. 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

    31. Image models, edges RS Gaborski

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

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

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

    35. RS Gaborski

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

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

    38. 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

    39. Finding Cars in ImagesTraining RS Gaborski

    40. Testing RS Gaborski

    41. Deformable Objects in Video RS Gaborski

    42. Simple Eye Model http://www.ap.stmarys.ca/demos/content/astronomy/eye_model/eye_model.html RS Gaborski

    43. Pin Hole Camera Model y p0( x0, y0, z0 ) y0 SENSOR  f z  yi=? ( z0-f ) z0 pi( xi, yi, zi ) tan  = yi / f tan  = y0 / ( z0 – f ) therefore, yi / f = y0 / ( z0 – f ) => yi = ( f * y0 ) / ( z0 – f ) RS Gaborski

    44. Loss of z InformationAll points of line p0-pi project to same point y p0( x0, y0, z0 ) y0 SENSOR  f z  yi=? ( z0-f ) z0 pi( xi, yi, zi ) tan  = yi / f tan  = y0 / ( z0 – f ) therefore, yi / f = y0 / ( z0 – f ) => yi = ( f * y0 ) / ( z0 – f ) RS Gaborski

    45. Digital Images • Matrix of numbers • Each number represents a picture element – ‘pixel’ • Pixels are parameterized by • x – y position • intensity (color or monochrome) • time • MATLAB is designed for processing matrices (Matrix Laboratory) RS Gaborski

    46. MATLAB • Any issues concerning using MATLAB on the CS department computers contact Sam Waters or Jim Craig in the CS System Admin office: System Administrators James "Linus" Craig; Username: jmc; 3599; 475-5254  Sam Waters; Username: srw; 3596; 475-4934 RS Gaborski

    47. MATLAB Tutorial • Complete MATLAB tutorial (not SIMULINK): http://www.mathworks.com/academia/student_center/tutorials/ RS Gaborski