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C280, Computer Vision Prof. Trevor Darrell Lecture 2: Image Formation.

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Presentation on theme: "C280, Computer Vision Prof. Trevor Darrell Lecture 2: Image Formation."— Presentation transcript:

1 C280, Computer Vision Prof. Trevor Darrell Lecture 2: Image Formation

2 Administrivia We’re now in 405 Soda… New office hours: Thurs. 5-6pm, 413 Soda. I’ll decide on waitlist decisions tomorrow. Any Matlab issues yet? Roster…

3 Physical parameters of image formation Geometric – Type of projection – Camera pose Optical – Sensor’s lens type – focal length, field of view, aperture Photometric – Type, direction, intensity of light reaching sensor – Surfaces’ reflectance properties Sensor – sampling, etc.

4 Physical parameters of image formation Geometric – Type of projection – Camera pose Optical – Sensor’s lens type – focal length, field of view, aperture Photometric – Type, direction, intensity of light reaching sensor – Surfaces’ reflectance properties Sensor – sampling, etc.

5 Perspective and art Use of correct perspective projection indicated in 1 st century B.C. frescoes Skill resurfaces in Renaissance: artists develop systematic methods to determine perspective projection (around ) Durer, 1525Raphael K. Grauman

6 Perspective projection equations 3d world mapped to 2d projection in image plane Forsyth and Ponce Camera frame Image plane Optical axis Focal length Scene / world points Scene point Image coordinates ‘’‘’ ‘’‘’

7 Homogeneous coordinates Is this a linear transformation? Trick: add one more coordinate: homogeneous image coordinates homogeneous scene coordinates Converting from homogeneous coordinates no—division by z is nonlinear Slide by Steve Seitz

8 Perspective Projection Matrix divide by the third coordinate to convert back to non- homogeneous coordinates Projection is a matrix multiplication using homogeneous coordinates: Slide by Steve Seitz Complete mapping from world points to image pixel positions?

9 Perspective projection & calibration Perspective equations so far in terms of camera’s reference frame…. Camera’s intrinsic and extrinsic parameters needed to calibrate geometry. Camera frame K. Grauman

10 Perspective projection & calibration Camera frame Intrinsic: Image coordinates relative to camera  Pixel coordinates Extrinsic: Camera frame  World frame World frame World to camera coord. trans. matrix (4x4) Perspective projection matrix (3x4) Camera to pixel coord. trans. matrix (3x3) = 2D point (3x1) 3D point (4x1) K. Grauman

11 Intrinsic parameters: from idealized world coordinates to pixel values Forsyth&Ponce Perspective projection W. Freeman

12 Intrinsic parameters But “pixels” are in some arbitrary spatial units W. Freeman

13 Intrinsic parameters Maybe pixels are not square W. Freeman

14 Intrinsic parameters We don’t know the origin of our camera pixel coordinates W. Freeman

15 Intrinsic parameters May be skew between camera pixel axes W. Freeman

16 Intrinsic parameters, homogeneous coordinates Using homogenous coordinates, we can write this as: or: In camera-based coords In pixels W. Freeman

17 Extrinsic parameters: translation and rotation of camera frame Non-homogeneous coordinates Homogeneous coordinates W. Freeman

18 Combining extrinsic and intrinsic calibration parameters, in homogeneous coordinates Forsyth&Ponce Intrinsic Extrinsic World coordinates Camera coordinates pixels W. Freeman

19 Other ways to write the same equation pixel coordinates world coordinates Conversion back from homogeneous coordinates leads to: W. Freeman

20 Calibration target Find the position, u i and v i, in pixels, of each calibration object feature point.

21 Camera calibration So for each feature point, i, we have: From before, we had these equations relating image positions, u,v, to points at 3-d positions P (in homogeneous coordinates): W. Freeman

22 Camera calibration Stack all these measurements of i=1…n points into a big matrix: W. Freeman

23 Showing all the elements: In vector form: Camera calibration W. Freeman

24 We want to solve for the unit vector m (the stacked one) that minimizes Q m = 0 The minimum eigenvector of the matrix Q T Q gives us that (see Forsyth&Ponce, 3.1), because it is the unit vector x that minimizes x T Q T Q x. Camera calibration W. Freeman

25 Once you have the M matrix, can recover the intrinsic and extrinsic parameters as in Forsyth&Ponce, sect Camera calibration W. Freeman

26 Recall, perspective effects… Far away objects appear smaller Forsyth and Ponce

27 Perspective effects

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29 Parallel lines in the scene intersect in the image Converge in image on horizon line Image plane (virtual) Scene pinhole

30 Projection properties Many-to-one: any points along same ray map to same point in image Points  ? – points Lines  ? – lines (collinearity preserved) Distances and angles are / are not ? preserved – are not Degenerate cases: – Line through focal point projects to a point. – Plane through focal point projects to line – Plane perpendicular to image plane projects to part of the image.

31 Weak perspective Approximation: treat magnification as constant Assumes scene depth << average distance to camera World points: Image plane

32 Orthographic projection Given camera at constant distance from scene World points projected along rays parallel to optical access

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35 2D

36 3D

37 Other types of projection Lots of intriguing variants… (I’ll just mention a few fun ones) S. Seitz

38 360 degree field of view… Basic approach – Take a photo of a parabolic mirror with an orthographic lens (Nayar) – Or buy one a lens from a variety of omnicam manufacturers… See S. Seitz

39 Tilt-shift Titlt-shift images from Olivo Barbieri and Photoshop imitationsOlivo Barbieriimitations S. Seitz

40 tilt, shift

41 Tilt-shift perspective correction

42 normal lenstilt-shift lens

43 Rollout Photographs © Justin Kerr Rotating sensor (or object) Also known as “cyclographs”, “peripheral images” S. Seitz

44 Photofinish S. Seitz

45 Physical parameters of image formation Geometric – Type of projection – Camera pose Optical – Sensor’s lens type – focal length, field of view, aperture Photometric – Type, direction, intensity of light reaching sensor – Surfaces’ reflectance properties Sensor – sampling, etc.

46 Pinhole size / aperture Smaller Larger How does the size of the aperture affect the image we’d get? K. Grauman

47 Adding a lens A lens focuses light onto the film –Rays passing through the center are not deviated –All parallel rays converge to one point on a plane located at the focal length f Slide by Steve Seitz focal point f

48 Pinhole vs. lens K. Grauman

49 Cameras with lenses focal point F optical center (Center Of Projection) A lens focuses parallel rays onto a single focal point Gather more light, while keeping focus; make pinhole perspective projection practical K. Grauman

50 Human eye Fig from Shapiro and Stockman Pupil/Iris – control amount of light passing through lens Retina - contains sensor cells, where image is formed Fovea – highest concentration of cones Rough analogy with human visual system:

51 Thin lens Rays entering parallel on one side go through focus on other, and vice versa. In ideal case – all rays from P imaged at P’. Left focus Right focus Focal length fLens diameter d K. Grauman

52 Thin lens equation Any object point satisfying this equation is in focus K. Grauman

53 Focus and depth of field Image credit: cambridgeincolour.com

54 Focus and depth of field Depth of field: distance between image planes where blur is tolerable Thin lens: scene points at distinct depths come in focus at different image planes. (Real camera lens systems have greater depth of field.) Shapiro and Stockman “circles of confusion”

55 Focus and depth of field How does the aperture affect the depth of field? A smaller aperture increases the range in which the object is approximately in focus Flower images from Wikipedia Slide from S. Seitz

56 Depth from focus [figs from H. Jin and P. Favaro, 2002] Images from same point of view, different camera parameters 3d shape / depth estimates

57 Field of view Angular measure of portion of 3d space seen by the camera Images from K. Grauman

58 As f gets smaller, image becomes more wide angle – more world points project onto the finite image plane As f gets larger, image becomes more telescopic – smaller part of the world projects onto the finite image plane Field of view depends on focal length from R. Duraiswami

59 Field of view depends on focal length Smaller FOV = larger Focal Length Slide by A. Efros

60 Vignetting

61 Vignetting “natural”: “mechanical”: intrusion on optical path

62 Chromatic aberration

63

64 Physical parameters of image formation Geometric – Type of projection – Camera pose Optical – Sensor’s lens type – focal length, field of view, aperture Photometric – Type, direction, intensity of light reaching sensor – Surfaces’ reflectance properties Sensor – sampling, etc.

65 Environment map

66 BDRF

67 Diffuse / Lambertian

68 Foreshortening

69 Specular reflection

70 Phong Diffuse+specular+ambient:

71 Physical parameters of image formation Geometric – Type of projection – Camera pose Optical – Sensor’s lens type – focal length, field of view, aperture Photometric – Type, direction, intensity of light reaching sensor – Surfaces’ reflectance properties Sensor – sampling, etc.

72 Digital cameras Film  sensor array Often an array of charge coupled devices Each CCD is light sensitive diode that converts photons (light energy) to electrons camera CCD array optics frame grabber computer K. Grauman

73 Historical context Pinhole model: Mozi ( BCE), Aristotle ( BCE) Principles of optics (including lenses): Alhacen ( CE) Camera obscura: Leonardo da Vinci ( ), Johann Zahn ( ) First photo: Joseph Nicephore Niepce (1822) Daguerréotypes (1839) Photographic film (Eastman, 1889) Cinema (Lumière Brothers, 1895) Color Photography (Lumière Brothers, 1908) Television (Baird, Farnsworth, Zworykin, 1920s) First consumer camera with CCD: Sony Mavica (1981) First fully digital camera: Kodak DCS100 (1990) Niepce, “La Table Servie,” 1822 CCD chip Alhacen’s notes Slide credit: L. Lazebnik K. Grauman

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75 Digital Sensors

76 Resolution sensor: size of real world scene element a that images to a single pixel image: number of pixels Influences what analysis is feasible, affects best representation choice. [fig from Mori et al]

77 Digital images Think of images as matrices taken from CCD array. K. Grauman

78 im[176][201] has value 164 im[194][203] has value 37 width 520 j=1 500 height i=1 Intensity : [0,255] Digital images K. Grauman

79 Color sensing in digital cameras Source: Steve Seitz Estimate missing components from neighboring values (demosaicing) Bayer grid

80 RG B Color images, RGB color space K. Grauman Much more on color in next lecture…

81 Physical parameters of image formation Geometric – Type of projection – Camera pose Optical – Sensor’s lens type – focal length, field of view, aperture Photometric – Type, direction, intensity of light reaching sensor – Surfaces’ reflectance properties Sensor – sampling, etc.

82 Summary Image formation affected by geometry, photometry, and optics. Projection equations express how world points mapped to 2d image. Homogenous coordinates allow linear system for projection equations. Lenses make pinhole model practical Photometry models: Lambertian, BRDF Digital imagers, Bayer demosaicing Parameters (focal length, aperture, lens diameter, sensor sampling…) strongly affect image obtained. K. Grauman

83 Slide Credits Bill Freeman Steve Seitz Kristen Grauman Forsyth and Ponce Rick Szeliski and others, as marked…

84 ` Next time Color Readings: – Forsyth and Ponce, Chapter 6 – Szeliski, 2.3.2


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