c280, computer vision - people › ~trevor › cs280notes... · 2009-09-05 · c280, computer...
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![Page 1: C280, Computer Vision - People › ~trevor › CS280Notes... · 2009-09-05 · C280, Computer Vision Prof. Trevor Darrell trevor@eecs.berkeley.edu LtLecture 2: Image FtiFormation](https://reader033.vdocument.in/reader033/viewer/2022053011/5f0ecf897e708231d4410d5e/html5/thumbnails/1.jpg)
C280 Computer VisionC280, Computer Vision
Prof. Trevor Darrell
[email protected]@eecs.berkeley.edu
L t 2 I F tiLecture 2: Image Formation
![Page 2: C280, Computer Vision - People › ~trevor › CS280Notes... · 2009-09-05 · C280, Computer Vision Prof. Trevor Darrell trevor@eecs.berkeley.edu LtLecture 2: Image FtiFormation](https://reader033.vdocument.in/reader033/viewer/2022053011/5f0ecf897e708231d4410d5e/html5/thumbnails/2.jpg)
AdministriviaAdministrivia
• We’re now in 405 SodaWe re now in 405 Soda…
• New office hours: Thurs. 5‐6pm, 413 Soda.
’ll d id i li d i i• I’ll decide on waitlist decisions tomorrow.
• Any Matlab issues yet?
• Roster…
![Page 3: C280, Computer Vision - People › ~trevor › CS280Notes... · 2009-09-05 · C280, Computer Vision Prof. Trevor Darrell trevor@eecs.berkeley.edu LtLecture 2: Image FtiFormation](https://reader033.vdocument.in/reader033/viewer/2022053011/5f0ecf897e708231d4410d5e/html5/thumbnails/3.jpg)
Physical parameters of image formation
• Geometric• Geometric– Type of projection– Camera posep
• Optical– Sensor’s lens type– focal length, field of view, aperture
• PhotometricT di ti i t it f li ht hi– Type, direction, intensity of light reaching sensor
– Surfaces’ reflectance properties
• SensorSensor– sampling, etc.
![Page 4: C280, Computer Vision - People › ~trevor › CS280Notes... · 2009-09-05 · C280, Computer Vision Prof. Trevor Darrell trevor@eecs.berkeley.edu LtLecture 2: Image FtiFormation](https://reader033.vdocument.in/reader033/viewer/2022053011/5f0ecf897e708231d4410d5e/html5/thumbnails/4.jpg)
Physical parameters of image formation
• Geometric• Geometric– Type of projection– Camera posep
• Optical– Sensor’s lens type– focal length, field of view, aperture
• PhotometricT di ti i t it f li ht hi– Type, direction, intensity of light reaching sensor
– Surfaces’ reflectance properties
• SensorSensor– sampling, etc.
![Page 5: C280, Computer Vision - People › ~trevor › CS280Notes... · 2009-09-05 · C280, Computer Vision Prof. Trevor Darrell trevor@eecs.berkeley.edu LtLecture 2: Image FtiFormation](https://reader033.vdocument.in/reader033/viewer/2022053011/5f0ecf897e708231d4410d5e/html5/thumbnails/5.jpg)
Perspective and art• Use of correct perspective projection indicated in 1st
century B C frescoescentury B.C. frescoes
• Skill resurfaces in Renaissance: artists develop systematic methods to determine perspectivesystematic methods to determine perspective projection (around 1480‐1515)
Durer, 1525RaphaelK. Grauman
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Perspective projection equations
• 3d world mapped to 2d projection in image plane
Image plane
Focal l thlength
Camera frame
Optical axis
Scene / world points‘’
‘’
Forsyth and Ponce
Scene point Image coordinates
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Homogeneous coordinatesIs this a linear transformation?
• no—division by z is nonlinear
Trick: add one more coordinate:
homogeneous image homogeneous scene coordinates coordinates
Converting from homogeneous coordinatesg f g
Slide by Steve Seitz
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Perspective Projection Matrix
• Projection is a matrix multiplication using homogeneous coordinates:coordinates:
⎤⎡⎥⎤
⎢⎡
⎤⎡ 0001 xx
⎥⎥⎥⎤
⎢⎢⎢⎡
=⎥⎥⎥
⎢⎢⎢
⎥⎥⎥⎤
⎢⎢⎢⎡
00100001
yx
zy
)','(zyf
zxf⇒
divide by the third coordinate to convert back to non‐h di t
⎥⎦⎢⎣⎥⎥
⎦⎢⎢
⎣⎥⎦⎢⎣ '/
10'/100 fz
zf
zz
homogeneous coordinates
Complete mapping from world points to image pixel
Slide by Steve Seitz
Complete mapping from world points to image pixel positions?
![Page 9: C280, Computer Vision - People › ~trevor › CS280Notes... · 2009-09-05 · C280, Computer Vision Prof. Trevor Darrell trevor@eecs.berkeley.edu LtLecture 2: Image FtiFormation](https://reader033.vdocument.in/reader033/viewer/2022053011/5f0ecf897e708231d4410d5e/html5/thumbnails/9.jpg)
Perspective projection & calibration
• Perspective equations so far in terms of camera’sreference frame….
• Camera’s intrinsic and extrinsic parameters needed to calibrate geometry.
Camera frame
K. Grauman
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Perspective projection & calibration
Extrinsic:Camera frame World frame
World frame
Intrinsic:Image coordinates relative to camera
Camera frame World frame
Camera frame
Image coordinates relative to camera Pixel coordinates
World to camera coord. trans. matrix
Perspectiveprojection matrix
(3x4)
Camera to pixel coord. trans. matrix =
2Dpoint(3x1)
3Dpoint(4x1)
(4x4)(3x4)
(3x3)(3x1) ( )
K. Grauman
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Intrinsic parameters: from idealized world coordinates to pixel valuesp
Forsyth&Ponce
xfu =Perspective projection
yfvz
f
=z
f
W. Freeman
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Intrinsic parameters
xu α=But “pixels” are in some bi i l i
yvz
α=
arbitrary spatial units
zW. Freeman
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Intrinsic parameters
xu α=Maybe pixels are not square
yvz
β=z
β
W. Freeman
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Intrinsic parameters
0uxu += αWe don’t know the origin of i l di
0
0
vyvz
+= β
our camera pixel coordinates
0zβ
W. Freeman
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Intrinsic parameters
v v′ ′
θu u ′
vv)()(
)sin(θθ
θ′′
=′
)( yx θ
May be skew between camera pixel axes
vuvuu )cot()cos( θθ −=′−=′
0)cot(
y
uzy
zxu +−=
β
θαα
0)sin(v
zyv +=
θβ
W. Freeman
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Intrinsic parameters, homogeneous coordinates
0)cot(
y
uzy
zxu +−=
β
θαα
0 )sin(
vzyv +=
θβ
⎛ ⎞ t(θ)⎛ ⎞ x⎛ ⎞ Using homogenous coordinates,we can write this as:
uv
⎛ ⎜ ⎜ ⎜
⎞ ⎟ ⎟ ⎟
=
α −α cot(θ) u0
0 βi (θ)
v0
00
⎛ ⎜ ⎜ ⎜
⎞ ⎟ ⎟ ⎟
xy
⎛ ⎜ ⎜ ⎜
⎞ ⎟ ⎟ ⎟
we can write this as:
1⎝ ⎜ ⎜
⎠ ⎟ ⎟ sin(θ)
0 0 1 0⎝ ⎜
⎠ ⎟ z
1⎝
⎜ ⎜
⎠
⎟ ⎟ or:
pp Crr K =In camera‐based coords
In pixels
W. Freeman
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Extrinsic parameters: translation d t ti f fand rotation of camera frame
tpRp CW
WCW
C rrr+= Non‐homogeneous
coordinates
Homogeneous ⎟⎞
⎜⎛
⎟⎞
⎜⎛ −−−⎟
⎞⎜⎛ |
coordinates
⎟⎟⎟⎟⎟
⎜⎜⎜⎜⎜
⎟⎟⎟⎟
⎜⎜⎜⎜
−−−−−=
⎟⎟⎟⎟⎟
⎜⎜⎜⎜⎜
ptRp WCW
CW
C rrr
|⎟⎟⎠
⎜⎜⎝
⎟⎠
⎜⎝⎟⎟
⎠⎜⎜⎝ 1000
W. Freeman
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Combining extrinsic and intrinsic calibration parameters in homogeneous coordinatesparameters, in homogeneous coordinates
CIntrinsicpixels
pp Crr K =
⎟⎞
⎜⎛
⎟⎞
⎜⎛ −−−⎟
⎞⎜⎛ |
World coordinatesCamera coordinates
p
Extrinsic
⎟⎟⎟⎟⎟⎞
⎜⎜⎜⎜⎜⎛
⎟⎟⎟⎟⎞
⎜⎜⎜⎜⎛
−−−−−=
⎟⎟⎟⎟⎟⎞
⎜⎜⎜⎜⎜⎛
ptRp WCW
CW
C rrr
|
|
⎟⎟⎠
⎜⎜⎝
⎟⎠
⎜⎝⎟⎟
⎠⎜⎜⎝ 1000
|
( ) ptRKp WCW
CW
rrr =0 0 0 1
Forsyth&PoncepMp W rr =
W. Freeman
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Other ways to write the same equationpixel coordinates
world coordinates
pMp W rr =
⎟⎞
⎜⎛
⎞⎛⎞⎛ xW
T pmu
Pmu r
r⋅
= 1
⎟⎟⎟⎟
⎜⎜⎜⎜
⎟⎟⎟⎞
⎜⎜⎜⎛
=⎟⎟⎟⎞
⎜⎜⎜⎛
...
...
2
1
Wy
Wx
T
T
pp
mm
vu
PmPm
ur
r
⋅⋅
2
3
⎟⎟⎠
⎜⎜⎝
⎟⎠
⎜⎝
⎟⎠
⎜⎝ 1
...1 3zT p
mPm
mv r⋅
=3
2
Conversion back from homogeneous coordinatesConversion back from homogeneous coordinates leads to:
W. Freeman
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Calibration target
Find the position, ui and vi, in pixels, of each
http://www.kinetic.bc.ca/CompVision/opti‐CAL.html
Find the position, ui and vi, in pixels, of each calibration object feature point.
![Page 21: C280, Computer Vision - People › ~trevor › CS280Notes... · 2009-09-05 · C280, Computer Vision Prof. Trevor Darrell trevor@eecs.berkeley.edu LtLecture 2: Image FtiFormation](https://reader033.vdocument.in/reader033/viewer/2022053011/5f0ecf897e708231d4410d5e/html5/thumbnails/21.jpg)
Camera calibration
PmPmu r
r
⋅⋅
=3
1From before, we had these equations relating image positions,u,v, to points at 3‐d positions P (in homogeneous
PmPmv r
r
⋅⋅
=3
2coordinates):
0)( Pr
So for each feature point, i, we have:
0)(0)(
32
31
=⋅−=⋅−
ii
ii
PmvmPmumr
W. Freeman
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Camera calibration
0)( 31 =⋅− ii Pmumr
rStack all these measurements of i=1…n points
0)( 32 =⋅− ii Pmvmr
into a big matrix:
⎞⎛⎟⎞
⎜⎛ − 00 PuP TTT
⎟⎟⎟⎞
⎜⎜⎜⎛
⎟⎟⎞
⎜⎜⎛
⎟⎟⎟⎞
⎜⎜⎜⎛
−−
00
00
1111
111
M
mPvPPuP
TTT
⎟⎟⎟⎟
⎜⎜⎜⎜=
⎟⎟
⎠⎜⎜
⎝⎟⎟⎟⎟
⎜⎜⎜⎜
−000
3
2 MLLL
mm
PuP Tnn
TTn
⎟⎟⎠
⎜⎜⎝⎟
⎠⎜⎝ − 00 PvP T
nnT
nT
W. Freeman
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⎟⎟⎟⎞
⎜⎜⎜⎛
=⎟⎟⎞
⎜⎜⎛
⎟⎟⎟⎟⎞
⎜⎜⎜⎜⎛
−−
00
00
2
1111
111
MLLL mmPvP
PuPTTT
TTTIn vector form:
Camera calibration
⎟⎟⎟⎟
⎠⎜⎜⎜⎜
⎝
⎟⎟
⎠⎜⎜
⎝⎟⎟⎟
⎠⎜⎜⎜
⎝ −−
00
00
3
2 M
mm
PvPPuP
Tnn
Tn
T
Tnn
TTn
⎞⎛
⎟⎟⎟⎟⎞
⎜⎜⎜⎜⎛
13
12
11
mmm
Showing all the elements:
⎟⎟⎞
⎜⎜⎛
⎟⎟⎟⎟⎟
⎜⎜⎜⎜⎜
⎟⎟⎞
⎜⎜⎛
−−−−−−−−
00
1000000001
21
14
13
1111111111
1111111111 mm
vPvPvPvPPPuPuPuPuPPP zyxzyx
⎟⎟⎟⎟⎟
⎠⎜⎜⎜⎜⎜
⎝
=
⎟⎟⎟⎟
⎜⎜⎜⎜
⎟⎟⎟⎟⎟
⎠⎜⎜⎜⎜⎜
⎝
−−−−00
0
1000000001
10000
24
23
221111111111
MLLL
mmm
PPPPPPuPuPuPuPPP
vPvPvPvPPP
nnznnynnxnnznynx
zyxzyx
⎟⎠
⎜⎝
⎟⎟⎟⎟⎟
⎜⎜⎜⎜⎜⎟
⎠⎜⎝ −−−− 010000
32
31
24
mmmvPvPvPvPPP nnznnynnxnnznynx
⎟⎟⎟
⎠⎜⎜⎜
⎝ 34
33
mm
W. Freeman
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⎟⎟⎟⎟⎞
⎜⎜⎜⎜⎛
13
12
11
mmm
Camera calibration
⎟⎟⎟⎟⎞
⎜⎜⎜⎜⎛
=⎟⎟⎟⎟⎟⎟
⎜⎜⎜⎜⎜⎜
⎟⎟⎟⎟⎞
⎜⎜⎜⎜⎛
−−−−−−−−
0
00
1000000001
22
21
14
13
1111111111
1111111111
MLLLmmmm
vPvPvPvPPPuPuPuPuPPP
zyxzyx
zyxzyx
⎟⎟⎟
⎠⎜⎜⎜
⎝
⎟⎟⎟⎟⎟⎟⎟
⎜⎜⎜⎜⎜⎜⎜
⎟⎟⎟
⎠⎜⎜⎜
⎝ −−−−−−−−
00
1000000001
32
31
24
23
mmmm
vPvPvPvPPPuPuPuPuPPP
nnznnynnxnnznynx
nnznnynnxnnznynx
⎟⎟⎟
⎠⎜⎜⎜
⎝ 34
33
mm
Q m = 0
We want to solve for the unit vector m (the stacked one)
Q m = 0
that minimizes 2QmThe minimum eigenvector of the matrix QTQ gives us that(see Forsyth&Ponce, 3.1), because it is the unit vector x that minimizes xT QTQ x.
W. Freeman
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Camera calibration
Once you have the M matrix, can recover the intrinsic and extrinsic parameters as in Forsyth&Ponce, sect. 3.2.2.
W. Freeman
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Recall, perspective effects…
• Far away objects appear smaller
Forsyth and Ponce
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Perspective effects
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Perspective effects
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Perspective effects• Parallel lines in the scene intersect in the image
• Converge in image on horizon line• Converge in image on horizon line
Image plane(virtual)(virtual)
pinhole
SceneScene
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Projection properties
• Many‐to‐one: any points along same ray map to same point in imagesame point in image
• Points ? – pointsp
• Lines ?– lines (collinearity preserved)
• Distances and angles are / are not ? preserved– are not
• Degenerate cases:– Line through focal point projects to a point.
Pl h h f l i j li– Plane through focal point projects to line
– Plane perpendicular to image plane projects to part of the image.
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Weak perspective
• Approximation: treat magnification as constant
A d th di t t• Assumes scene depth << average distance to camera
World points:
Image plane
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Orthographic projection• Given camera at constant distance from scene
• World points projected along rays parallel to optical• World points projected along rays parallel to optical access
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2D2D
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3D3D
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Other types of projectionOther types of projection
• Lots of intriguing variantsLots of intriguing variants…
• (I’ll just mention a few fun ones)
S. Seitz
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360 degree field of view…360 degree field of view…
• Basic approachBasic approach– Take a photo of a parabolic mirror with an orthographic lens (Nayar)– Or buy one a lens from a variety of omnicam manufacturers…
S http://www cis upenn edu/~kostas/omni html• See http://www.cis.upenn.edu/ kostas/omni.html
S. Seitz
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Tilt‐shift
http://www.northlight-images.co.uk/article_pages/tilt_and_shift_ts-e.html
Titlt-shift images from Olivo Barbieriand Photoshop imitations S. Seitz
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tilt, shifttilt, shift
http://en.wikipedia.org/wiki/Tilt‐shift_photography
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Tilt‐shift perspective correctionTilt shift perspective correction
http://en.wikipedia.org/wiki/Tilt‐shift_photography
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normal lens tilt‐shift lens
http://www.northlight‐images.co.uk/article_pages/tilt_and_shift_ts‐e.html
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Rotating sensor (or object)Rotating sensor (or object)
Rollout Photographs © Justin Kerr http://research.famsi.org/kerrmaya.html
Also known as “cyclographs”, “peripheral images”
S. Seitz
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PhotofinishPhotofinish
S. Seitz
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Physical parameters of image formation
• Geometric• Geometric– Type of projection– Camera posep
• Optical– Sensor’s lens type– focal length, field of view, aperture
• PhotometricT di ti i t it f li ht hi– Type, direction, intensity of light reaching sensor
– Surfaces’ reflectance properties
• SensorSensor– sampling, etc.
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Pinhole size / apertureHow does the size of the aperture affect the image we’d get?
Larger
Smaller
K. Grauman
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Adding a lens
focal point
f
• A lens focuses light onto the film
f
• 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 p y g p plocated at the focal length f
Slide by Steve Seitz
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Pinhole vs. lens
K. Grauman
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Cameras with lenses
F
focal pointoptical center
(Center Of Projection)(Center Of Projection)
• A lens focuses parallel rays onto a single focal point• A lens focuses parallel rays onto a single focal point
• Gather more light, while keeping focus; make pinhole perspective projection practicalpinhole perspective projection practical
K. Grauman
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Human eye
Rough analogy with human visual system:
Pupil/Iris – control amount of light passing through lens
Retina ‐ contains sensor cells where image iscells, where image is formed
Fovea – highest concentration of cones
Fig from Shapiro and Stockman
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Thin lens
Thin lensRays entering parallel on one side go through f h d iLeft focus Ri ht f focus on other, and vice versa.
In ideal case – all rays
Left focus Right focus
In ideal case all rays from P imaged at P’.
Focal length fLens diameter d
K. Grauman
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Thin lens equation
111vuf
+=
u v
• Any object point satisfying this equation is in focusin focus
K. Grauman
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Focus and depth of field
Image credit: cambridgeincolour.com
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Focus and depth of field
• Depth of field: distance between image planes where blur is tolerableblur is tolerable
Thin lens: scene points atThin lens: scene points at distinct depths come in focus at different image planesplanes.
(Real camera lens systems have greater depth of field.)
“circles of confusion”
Shapiro and Stockman
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Focus and depth of field• How does the aperture affect the depth of field?
• A smaller aperture increases the range in which the object i i t l i fis approximately in focus
Flower images from Wikipedia http://en.wikipedia.org/wiki/Depth_of_field Slide from S. Seitz
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Depth from focus
Images from sameImages from same point of view, different camera parameters
3d shape / depth3d shape / depth estimates
[figs from H. Jin and P. Favaro, 2002]
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Field of view
• AngularAngular measure of portion of 3d space seen by the camera
Images from http://en.wikipedia.org/wiki/Angle_of_view K. Grauman
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Field of view depends on focal length
• As f gets smaller, image becomes more wide anglebecomes 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– smaller part of the world projects onto the finite image plane
from R. Duraiswami
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Field of view depends on focal length
Smaller FOV = larger Focal LengthSlide by A. Efros
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VignettingVignetting
http://www.ptgui.com/examples/vigntutorial.htmlhttp://www.tlucretius.net/Photo/eHolga.html
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VignettingVignetting
• “natural”:natural :
“ h i l” i i i l h• “mechanical”: intrusion on optical path
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Chromatic aberrationChromatic aberration
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Chromatic aberrationChromatic aberration
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Physical parameters of image formation
• Geometric• Geometric– Type of projection– Camera posep
• Optical– Sensor’s lens type– focal length, field of view, aperture
• PhotometricT di ti i t it f li ht hi– Type, direction, intensity of light reaching sensor
– Surfaces’ reflectance properties
• SensorSensor– sampling, etc.
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Environment mapEnvironment map
http://www.sparse.org/3d.html
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BDRFBDRF
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Diffuse / LambertianDiffuse / Lambertian
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ForeshorteningForeshortening
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Specular reflectionSpecular reflection
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PhongPhong
• Diffuse+specular+ambient:Diffuse+specular+ambient:
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Physical parameters of image formation
• Geometric• Geometric– Type of projection– Camera posep
• Optical– Sensor’s lens type– focal length, field of view, aperture
• PhotometricT di ti i t it f li ht hi– Type, direction, intensity of light reaching sensor
– Surfaces’ reflectance properties
• SensorSensor– sampling, etc.
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Digital cameras
• Film sensor array
• Often an array of charge coupled devices
• Each CCD is light sensitive diode that converts photons (l h ) l(light energy) to electrons
cameraCCD array
optics frame grabber
computer
K. Grauman
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Historical contextHistorical context• Pinhole model:Mozi (470‐390 BCE),
Aristotle (384‐322 BCE)
• Principles of optics (including lenses):Principles of optics (including lenses):Alhacen (965‐1039 CE)
• Camera obscura: Leonardo da Vinci (1452‐1519) Johann Zahn (1631‐1707)
Alhacen’s notes
(1452‐1519), Johann Zahn (1631‐1707)
• First photo: Joseph Nicephore Niepce (1822)
• Daguerréotypes (1839)
• Photographic film (Eastman, 1889)
• Cinema (Lumière Brothers, 1895)
• Color Photography (Lumière Brothers, 1908)
Niepce, “La Table Servie,” 1822
Color Photography (Lumière Brothers, 1908)
• Television (Baird, Farnsworth, Zworykin, 1920s)
• First consumer camera with CCD: Sony Mavica (1981)Sony Mavica (1981)
• First fully digital camera: Kodak DCS100 (1990)
CCD chipSlide credit: L. Lazebnik K. Grauman
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Digital SensorsDigital Sensors
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Resolution
• sensor: size of real world scene element a that images to a single pixelimages to a single pixel
• image: number of pixels
• Influences what analysis is feasible affects best• Influences what analysis is feasible, affects best representation choice.
[fig from Mori et al][fig from Mori et al]
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Digital images
Think of images as matrices taken from CCDmatrices taken from CCD array.
K. Grauman
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widthDigital images
width 520j=1i=1Intensity : [0,255]
500 height
im[176][201] has value 164 im[194][203] has value 37
K. Grauman
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Color sensing in digital cameras
Estimate missing
Bayer grid
Estimate missing components from neighboring values(demosaicing)(demosaicing)
Source: Steve Seitz
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Color images, RGBColor images, RGB color space
R G BR G B
K. GraumanMuch more on color in next lecture…
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Physical parameters of image formation
• Geometric• Geometric– Type of projection– Camera posep
• Optical– Sensor’s lens type– focal length, field of view, aperture
• PhotometricT di ti i t it f li ht hi– Type, direction, intensity of light reaching sensor
– Surfaces’ reflectance properties
• SensorSensor– sampling, etc.
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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, BRDFy ,
• Digital imagers, Bayer demosaicing
Parameters (focal length aperture lens diameterParameters (focal length, aperture, lens diameter, sensor sampling…) strongly affect image obtained.
K. Grauman
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Slide CreditsSlide Credits
• Bill FreemanBill Freeman
• Steve Seitz
i G• Kristen Grauman
• Forsyth and Ponce
• Rick Szeliski
• and others as markedand others, as marked…
![Page 84: C280, Computer Vision - People › ~trevor › CS280Notes... · 2009-09-05 · C280, Computer Vision Prof. Trevor Darrell trevor@eecs.berkeley.edu LtLecture 2: Image FtiFormation](https://reader033.vdocument.in/reader033/viewer/2022053011/5f0ecf897e708231d4410d5e/html5/thumbnails/84.jpg)
Next timeNext time
`ColorColorReadings:
F h d P Ch 6– Forsyth and Ponce, Chapter 6
– Szeliski, 2.3.2