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3D Computer Vision: CSc 83020. Instructor: Ioannis Stamos istamos (at) hunter.cuny.edu Office Hours: Tuesdays 4-6.

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Presentation on theme: "3D Computer Vision: CSc 83020. Instructor: Ioannis Stamos istamos (at) hunter.cuny.edu Office Hours: Tuesdays 4-6."— Presentation transcript:

1 3D Computer Vision: CSc 83020

2 Instructor: Ioannis Stamos istamos (at) hunter.cuny.edu Office Hours: Tuesdays 4-6 (at Hunter) or by appoitnment Office: 1090G Hunter North (69 th street bw. Park and Lex.) Computer Vision Lab: 1090E Hunter North Course web page: 3D Computer Vision: CSc 83020

3 Goals To familiarize you with basic the techniques and jargon in the field To enable you to solve computer vision problems To let you experience (and appreciate!) the difficulties of real-world computer vision To get you excited!

4 Class Policy You have to –Turn in all assignments (60% of grade) –Complete a final project (30% of grade) –Actively participate in class (10% of grade) Late policy –Six late days (but not for final project) Teaming –For final project you can work in groups of 2

5 About me 11 th year at Hunter and the Graduate Center Graduated from Columbia in ’01 –CS Ph.D. Research Areas: –Computer Vision –3D Modeling –Computer Graphics

6 Books Computer Vision: Algorithms and Applications, Richard Szeliski, 2010 (available online for free) Robot Vision B. K. P. Horn, The MIT Press (great classic book) Introductory Techniques for 3-D Computer Vision Emanuele Trucco and Alessandro Verri, Prentice Hall, 1998 (algorithmic perspective) Computer Vision A Modern Approach David A. Forsyth, Jean Ponce, Prentice Hall 2003 An Invitation to 3-D Vision Yi Ma, Stefano Soatto, Jana Kosecka, S. Shankar Sastry Springer Three-Dimensional Computer Vision: A Geometric Viewpoint Olivier Faugeras The MIT Press, 1996.

7 Journals/Web International Journal of Computer Vision. Computer Vision and Image Understanding. IEEE Trans. on Pattern Analysis and Machine Intelligence. SIGGRAPH (mostly Graphics) (CMU’s Robotic Institute)http://www.ri.cmu.edu/ (The Vision Home Page) (CV Online) (Annotated CV Bibliography)

8 Class History Based on class taught at Columbia University by Prof. Shree Nayar. New material reflects modern approach. Taught similar class at Hunter Taught “3D Photography” class at the Graduate Center of CUNY. My active research area –Funded by the National Science Foundation

9 Class Schedule Check class website Final project proposals –Due Nov. 7 –Design your own or check list of possible projects on class website Final project presentations and report –May 16 (last class)

10 What is Computer Vision? Physical 3D World Illumination Vision System Scene Description Measuring Visual Information Sensors Images or Video

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

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

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

14 Cont. Vision is automating visual processes (Ball & Brown). Vision is an information processing task (Marr). Vision is inverting image formation (Horn). Vision is inverse graphics. Vision looks easy, but is difficult. Vision is difficult, but it is fun (Kanade). Vision is useful.

15 Some Applications Industrial –Material Handling –Inspection –Assembly

16 Some Applications Autonomous Navigation

17 Vision for Graphics Film Industry Urban Planning E-commerce Virtual Reality Some Applications

18 Realistic 3D experience –Google Earth –Microsoft Photosynth

19 More Applications! Optical Character Recognition (OCR) Visual Databases (images or movies) –Searching for image content Face Recognition (security) Iris Recognition (security) Traffic Monitoring Systems Many more…

20 Vision deals with images

21 Images Look Nice…

22 Ioannis Stamos – CSc Spring 2007 Images Look Nice…

23 ...Essentially a 2D array of numbers

24 Low-Level or “Early” Vision Considers local properties of an image “There’s an edge!” Szymon Rusinkiewicz, Princeton. From: Szymon Rusinkiewicz, Princeton.

25 Mid-Level Vision Grouping and segmentation “There’s an object and a background!”

26 High-Level Vision Recognition “It’s a chair!”

27 Humans Vision is easy for us. But how do we do it?

28 Human Vision: Illusions Fraser’s spiral (Fraser 1908)

29 Illusions Hering Illusion (1861) Wundt Illusion (1896) Zölner Illusion (1860)

30 Visual Ambiguities Young-Girl/Old-Woman

31 Visual Ambiguities From NALWA.

32 Visual Ambiguities

33

34

35 Seeing and Thinking Kanizsa (1979)

36 Syllabus Overview

37 Image Formation and Optics p Light Source Object Surface Lens CCD Array P Surface normal Projection of 3-D World on a 2-D plane

38 Lenses Ray of light Optical Axis

39 Image Sensors/Camera Models Typical 512x512 CCD array 512 (10.25mm) One Pixel 20μm Imaging Area 262,144 pixels Convert Optical Images To Electrical Signals.

40 Filtering = g h f

41 Ioannis Stamos – CSc Spring 2007 Image Features Detecting intensity changes in the image

42 Ioannis Stamos – CSc Spring 2007 Grouping image features Finding continuous lines from edge segments

43 Camera Calibration Xw Yw Zw World Coordinate Frame Xc Yc Zc Camera Coordinate Frame Image Coordinate Frame Pixel Coordinates Intrinsic Parameters Extrinsic Parameters

44 Shape from X –Stereo –Motion –Shading –Texture foreshortening

45 Binocular Stereo depth map

46 Active Sensing Lens Sheet of light CCD array Sources of error: 1) grazing angle, 2) object boundaries.

47 Ioannis Stamos – CSc Spring 2007 Shape from Shading Three-dimensional shape from a single image.

48 Ioannis Stamos – CSc Spring 2007 Motion (optical flow) Determining the movement of scene objects

49 Reflectance and Color Why do these spheres look different?

50 Object Recognition Learning visual appearance. Real-time object recognition.

51 Cootes et al. Template-Based Methods

52 Some Vision Systems…

53 Example 2: Structure From Motion Slide courtesy of Sebastian Thrun Stanford

54 Example 2: Structure From Motion Slide courtesy of Sebastian Thrun Stanford

55 Example 2: Structure From Motion Slide courtesy of Sebastian Thrun Stanford

56 Example 2: Structure From Motion Slide courtesy of Sebastian Thrun Stanford

57 Example 2: Structure From Motion Slide courtesy of Sebastian Thrun Stanford

58 Example 4: 3D Modeling Drago Anguelov Slide courtesy of Sebastian Thrun Stanford

59 Example 5: Segmentation Slide courtesy of Sebastian Thrun Stanford

60 Example 6: Classification Slide courtesy of Sebastian Thrun Stanford

61 Example 6: Classification Slide courtesy of Sebastian Thrun Stanford

62 Real-world Applications Osuna et al:

63 Ioannis Stamos – CSc Spring 2007 Range Scanning Outdoor Structures

64 Data Acquisition Spot laser scanner. Time of flight. Max Range: 100m. Scanning time: 20 minutes for 1000 x1000 points. Accuracy: 6mm.

65 Video

66 Latest Video

67 Inserting models in Google Earth

68 Dynamic Scenes Image sequence (CMU, Virtualized Reality Project)

69 Dynamic Scenes Dynamic 3D model.

70 Dynamic Scenes Dynamic texture-mapped model.

71 Scanning the David Marc Levoy, Stanford height of gantry: 7.5 meters weight of gantry: 800 kilograms

72 Ioannis Stamos – CSc Spring 2007 Statistics about the scan 480 individually aimed scans 2 billion polygons 7,000 color images 32 gigabytes 30 nights of scanning 22 people

73 Head of Michelangelo’s David photograph1.0 mm computer model

74 Ioannis Stamos – CSc Spring 2007 David’s left eye holes from Michelangelo’s drillartifacts from space carving noise from laser scatter 0.25 mm modelphotograph

75 What do you think?


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