IIIT-B Computer Vision, Fall 2006 Lecture 1 Introduction to Computer Vision

1. IIIT-B Computer Vision, Fall 2006Lecture 1 Introduction to Computer Vision Arvind Lakshmikumar Technology Manager, Sarnoff Corporation Adjunct Faculty, IIIT-B

2. Course Overview • Introduction to vision • Case Studies of Applied Vision • Automotive Safety • Autonomous Navigation • Industrial Inspection • Medical Imaging • Entertainment • Image Formation • About Cameras • Image Processing • Geometric Vision • Camera Motion • Paper readings

3. Computer Graphics Output Image Model Synthetic Camera (slides courtesy of Michael Cohen)

4. Computer Vision Output Model Real Scene Real Cameras (slides courtesy of Michael Cohen)

5. Combined Output Image Real Scene Model Synthetic Camera Real Cameras (slides courtesy of Michael Cohen)

6. How to infer salient properties of 3-D world from time-varying 2-D image projection The Vision Problem ¤ What is salient? ¤ How to deal with loss of information going from 3-D to 2-D?

7. Why study Computer Vision? • Images and movies are everywhere • Fast-growing collection of useful applications • building representations of the 3D world from pictures • automated surveillance (who’s doing what) • movie post-processing • face finding • Various deep and attractive scientific mysteries • how does object recognition work? • Greater understanding of human vision

8. Properties of Vision • One can “see the future” • Cricketers avoid being hit in the head • There’s a reflex --- when the right eye sees something going left, and the left eye sees something going right, move your head fast. • Gannets pull their wings back at the last moment • Gannets are diving birds; they must steer with their wings, but wings break unless pulled back at the moment of contact. • Area of target over rate of change of area gives time to contact.

9. Properties of Vision • 3D representations are easily constructed • There are many different cues. • Useful • to humans (avoid bumping into things; planning a grasp; etc.) • in computer vision (build models for movies). • Cues include • multiple views (motion, stereopsis) • texture • shading

10. Properties of Vision • People draw distinctions between what is seen • “Object recognition” • This could mean “is this a fish or a bicycle?” • It could mean “is this George Washington?” • It could mean “is this someone I know?” • It could mean “is this poisonous or not?” • It could mean “is this slippery or not?” • It could mean “will this support my weight?” • Great mystery • How to build programs that can draw useful distinctions based on image properties.

11. Part I: The Physics of Imaging • How images are formed • Cameras • What a camera does • How to tell where the camera was • Light • How to measure light • What light does at surfaces • How the brightness values we see in cameras are determined • Color • The underlying mechanisms of color • How to describe it and measure it

12. Part II: Early Vision in One Image • Representing small patches of image • For three reasons • We wish to establish correspondence between (say) points in different images, so we need to describe the neighborhood of the points • Sharp changes are important in practice --- known as “edges” • Representing texture by giving some statistics of the different kinds of small patch present in the texture. • Tigers have lots of bars, few spots • Leopards are the other way

13. Representing an image patch • Filter outputs • essentially form a dot-product between a pattern and an image, while shifting the pattern across the image • strong response -> image locally looks like the pattern • e.g. derivatives measured by filtering with a kernel that looks like a big derivative (bright bar next to dark bar)

14. Convolve this image To get this With this kernel

15. Texture • Many objects are distinguished by their texture • Tigers, cheetahs, grass, trees • We represent texture with statistics of filter outputs • For tigers, bar filters at a coarse scale respond strongly • For cheetahs, spots at the same scale • For grass, long narrow bars • For the leaves of trees, extended spots • Objects with different textures can be segmented • The variation in textures is a cue to shape

16. Part III: Early Vision in Multiple Images • The geometry of multiple views • Where could it appear in camera 2 (3, etc.) given it was here in 1 (1 and 2, etc.)? • Stereopsis • What we know about the world from having 2 eyes • Structure from motion • What we know about the world from having many eyes • or, more commonly, our eyes moving.

17. Part IV: Mid-Level Vision • Finding coherent structure so as to break the image or movie into big units • Segmentation: • Breaking images and videos into useful pieces • E.g. finding video sequences that correspond to one shot • E.g. finding image components that are coherent in internal appearance • Tracking: • Keeping track of a moving object through a long sequence of views

18. Part V: High Level Vision (Geometry) • The relations between object geometry and image geometry • Model based vision • find the position and orientation of known objects • Smooth surfaces and outlines • how the outline of a curved object is formed, and what it looks like • Aspect graphs • how the outline of a curved object moves around as you view it from different directions • Range data

19. Part VI: High Level Vision (Probabilistic) • Using classifiers and probability to recognize objects • Templates and classifiers • how to find objects that look the same from view to view with a classifier • Relations • break up objects into big, simple parts, find the parts with a classifier, and then reason about the relationships between the parts to find the object. • Geometric templates from spatial relations • extend this trick so that templates are formed from relations between much smaller parts

20. Applications: Factory Inspection Cognex’s “CapInspect” system: Low-level image analysis: Identify edges, regions Mid-level: Distinguish “cap” from “no cap” Estimation: What are orientation of cap, height of liquid?

21. Applications: Face Detection courtesy of H. Rowley How is this like the bottle problem on the previous slide?

22. Applications: Text Detection & Recognition from J. Zhang et al. Similar to face finding: Where is the text and what does it say? Viewing at an angle complicates things...

23. Applications: MRI Interpretation from W. Wells et al. Coronal slice of brain Segmented white matter

24. Detection and Recognition: How? • Build models of the appearance characteristics (color, texture, etc.) of all objects of interest • Detection: Look for areas of image with sufficiently similar appearance to a particular object • Recognition: Decide which of several objects is most similar to what we see • Segmentation: “Recognize” every pixel

25. Applications: Football First-Down Line courtesy of Sportvision

26. Applications: Virtual Advertising courtesy of Princeton Video Image

27. First-Down Line, Virtual Advertising: How? • Where should message go? • Sensors that measure pan, tilt, zoom and focus are attached to calibrated cameras at surveyed positions • Knowledge of the 3-D position of the line, advertising rectangle, etc. can be directly translated into where in the image it should appear for a given camera • What pixels get painted? • Occluding image objects like the ball, players, etc. where the graphic is to be put must be segmented out. These are recognized by being a sufficiently different color from the background at that point. This allows pixel-by-pixel compositing.

28. Applications: Inserting Computer Graphics with a Moving Camera Opening titles from the movie “Panic Room” How does motion complicate things?

29. Applications: Inserting Computer Graphics with a Moving Camera courtesy of 2d3

30. CG Insertion with a Moving Camera: How? • This technique is often called matchmove • Once again, we need camera calibration, but also information on how the camera is moving—its egomotion. This allows the CG object to correctly move with the real scene, even if we don’t know the 3-D parameters of that scene. • Estimating camera motion: • Much simpler if we know camera is moving sideways (e.g., some of the “Panic Room” shots), because then the problem is only 2-D • For general motions: By identifying and following scene features over the entire length of the shot, we can solve retrospectively for what 3-D camera motion would be consistent with their 2-D image tracks. Must also make sure to ignore independently moving objects like cars and people.

31. Applications: Rotoscoping 2d3’s Pixeldust

32. Applications: Motion Capture Vicon software: 12 cameras, 41 markers for body capture; 6 zoom cameras, 30 markers for face

33. Applications: Motion Capture without Markers courtesy of C. Bregler What’s the difference between these two problems?

34. Motion Capture: How? • Similar to matchmove in that we follow features and estimate underlying motion that explains their tracks • Difference is that the motion is not of the camera but rather of the subject (though camera could be moving, too) • Face/arm/person has more degrees of freedom than camera flying through space, but still constrained • Special markers make feature identification and tracking considerably easier • Multiple cameras gather more information

35. Applications: Image-Based Modeling courtesy of P. Debevec Façade project: UC Berkeley Campanile

36. Image-Based Modeling: How? • 3-D model constructed from manually-selected line correspondences in images from multiple calibrated cameras • Novel views generated by texture-mapping selected images onto model

37. Applications: Robotics Autonomous driving: Lane & vehicle tracking (with radar)

38. Why is Vision Interesting? • Psychology • ~ 50% of cerebral cortex is for vision. • Vision is how we experience the world. • Engineering • Want machines to interact with world. • Digital images are everywhere.

39. Vision is inferential: Light (http://www-bcs.mit.edu/people/adelson/checkershadow_illusion.html)

40. Vision is inferential: Light (http://www-bcs.mit.edu/people/adelson/checkershadow_illusion.html)

41. Vision is Inferential: Geometry

42. Computer Vision • Inference  Computation • Building machines that see • Modeling biological perception

43. Boundary Detection: Local cues

44. Boundary Detection: Local cues

47. Boundary Detection http://www.robots.ox.ac.uk/~vdg/dynamics.html

48. Boundary Detection Finding the Corpus Callosum (G. Hamarneh, T. McInerney, D. Terzopoulos)

49. (Sharon, Balun, Brandt, Basri)