Strongly coupled problems - Computer Graphics at Stanford University

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http://www.cs.tau.ac.il/~wolf/OR/img/street_annotated.jpg
CS 148, Summer 2012
Introduction to Computer Graphics and Imaging
Justin Solomon
Final exam
Saturday 8/18/12, 12:15pm-3:15pm
Makeup: Thursday 8/16/12, 1pm-4pm
Two double-sided 8.5x11 sheets of notes
Homework 6
Due Tuesday, August 14, 11:59pm
Must be returned by Friday, August 17
ILM's VFX Pipeline and the
Future of Performance
Capture
Hao Li
Industrial Light and Magic
www.youtube.com/watch?v=UKKv2fhX2TM
Graphics produces images.
Vision analyzes images.
Inverse problems
Graphics produces images.
Vision analyzes images.
Inverse problems
Strongly coupled
problems
http://delivery.acm.org/10.1145/1110000/1103923/cs22.pdf?ip=171.67.216.21&acc=ACTIVE%20SERVICE&CFID=102787391&CFTOKEN=32277993&__acm__=1344483871_d2aba55345e66ac180cfb4b589ddb7ad
Strongly coupled problems
http://www.engineeringspecifier.com/public/primages/pr1200.jpg
http://twr.cs.kuleuven.be/images/pointCloudProcessing.jpg
http://www.ibe.kagoshima-u.ac.jp/~cgv/research/MVS.html
http://research.microsoft.com/en-us/um/people/jiansun/papers/dehaze_cvpr2009.pdf
http://graphics.cs.cmu.edu/projects/scene-completion/scene-completion.pdf
http://www.mpi-inf.mpg.de/~thormae/paper/CVPR11.pdf
http://www.youtube.com/watch?v=hnP7G7ahuus
http://www.youtube.com/watch?v=OmTCxff-DSk
http://graphics.cs.cmu.edu/projects/imageshaving/nguyen_eurographics_08.
http://graphics.cs.cmu.edu/projects/imageshaving/nguyen_eurographics_08.
Meaningful
RGB RGB RGB RGB RGB RGB RGB
Not meaningful
http://upload.wikimedia.org/wikipedia/commons/1/16/Cactus_flower_unidentified.jpg
http://www.visitingdc.com/images/eiffel-tower-picture-2.jpg http://wondrouspics.com/wp-content/uploads/2011/10/eiffel-tower.jpg
Alignment
http://www.visitingdc.com/images/eiffel-tower-picture.jpg http://www.visitingdc.com/images/eiffel-tower-at-night.jpg http://www.hdwallpapers.in/walls/eiffel_tower_at_night_paris_francenormal.jpg
Lighting and materials
http://a4.ec-images.myspacecdn.com/images01/24/69bf8c4862e568739d8e282570465059/l.jpg http://cache.daylife.com/imageserve/0eRdbnFger62Z/439x.jpg
http://gallery.weddingbee.com/photo/eloped-getting-married-in-front-of-the-eiffel-tower-in-paris http://specialtysites.typepad.com/.a/6a01127970a11f28a40120a5e8924b970b-320wi
Occlusion
http://ars.sciencedirect.com/content/image/1-s2.0-S0097849305000464-gr15.jpg
http://graphics.cs.cmu.edu/projects/stvk/
Deformation
http://upload.wikimedia.org/wikipedia/commons/thumb/5/53/Maurice_koechlin_pylone.jpg/220px-Maurice_koechlin_pylone.jpg http://www.papertoys.com/images/eiffel.gif http://farm7.static.flickr.com/6045/6230445674_7259927bcf.jpg
http://cdn1.retronaut.co/wp-content/uploads/2010/07/Eiffel-Tower-6.jpg http://www.visitingdc.com/images/eiffel-tower-las-vegas.jpg https://s3.amazonaws.com/luuux-original-files/bookmarklet_uploaded/eiffel-tower-2.jpg
Instances
http://vision.stanford.edu/teaching/cs231a/lecture/lecture%2019_advanced_topics_cs231a.pdf
Computer graphics is a
humongous
field.
Computer vision is a
^
humongous
field.
Provide examples of vision
techniques applied in
graphics.
http://vision.middlebury.edu/flow/floweval-ijcv2011.pdf
Image alignment
http://digital-photography-school.com/wp-content/uploads/2009/03/exposure-fusion1.jpg
Fuse exposures to one floating-point image
http://research.microsoft.com/en-us/um/redmond/projects/flashnoflash/flash_no_flash%20%28web%29.pdf
http://vision.in.tum.de/research/optical_flow_estimation
Optical flow
http://vision.in.tum.de/research/optical_flow_estimation
Optical flow
Flat texture
gives no
motion cues!
http://upload.wikimedia.org/wikipedia/commons/f/f0/Aperture_problem_animated.gif
http://2.bp.blogspot.com/-6LnK6MaXArg/TiLPPgmmyMI/AAAAAAAABdU/b87U2CDESfo/s1600/barber-shop-pole.jpg
Stanford CS448F, Andrew Adams
Global alignment
Rotate
Zoom
Translate
code.ucsd.edu/pcosman/253video1.ppt
Rotate
Zoom
Translate
code.ucsd.edu/pcosman/253video1.ppt
p
~ 7! M~
p + ~t
Affine
Similarity
Rigid
http://faculty.cs.tamu.edu/schaefer/research/mls.pdf
p~1 7! ~q1
p~2 7! ~q2
p~3 7! ~q3
¢ ¢ ¢ 7! ¢ ¢ ¢
p~n 7! ~qn
Stanford CS448F, Andrew Adams
Example points
p~1 7! ~q1
p~2 7! ~q2
p~3 7! ~q3
¢ ¢ ¢ 7! ¢ ¢ ¢
p~n 7! ~qn
Two differences:
1.Might make mistakes
matching handles
2.Global motion
Example points
Flat
Edge
Corner
No change in all
directions
No change
along the edge
direction
Significant
change in all
directions
MIT 6.882, Bill Freeman
Harris corner detector
E(¢x; ¢y) =
X
x;y
w(x; y) [I(x + ¢x; y + ¢y) ¡ I(x; y)]
Autocorrelation:
How well shifting by (Δx, Δy) preserves
image in area covered by w.
w(x; y) =
or
2
E(¢x; ¢y) =
X
x;y
w(x; y) [I(x + ¢x; y + ¢y) ¡ I(x; y)]
@I
@I
I(x + ¢x; y + ¢y) ¼ I(x; y) +
¢x +
¢y
@x
@y
E(¢x; ¢y) ¼
M=
¡
¢x ¢y
X
x;y
w(x; y)
¢
µ
M
µ
Ix2
Ix Iy
¢x
¢y
¶
Ix Iy
Iy2
;
¶
2
2
2
R = det M ¡ ·(tr M) = ¸1¸2 ¡ ·(¸1 + ¸2)
E(¢x; ¢y) ¼
M=
¡
¢x ¢y
X
x;y
w(x; y)
¢
µ
M
µ
Ix2
Ix Iy
¢x
¢y
¶
Ix Iy
Iy2
;
¶
R
Threshold
Local max
Features
Features
Features
Match points
Find transformation
while ignoring outliers
Rotation invariance
Compute relative to gradient direction
rI = (Ix; Iy )
Scale invariance
Rescale w and its analogs multiple times
http://media.wiley.com/wires/WICS2.2/mfig001.jpg
Match descriptors
Invariant to:
• Rotation
• Scale
• Intensity change
• (Some) affine motion
http://ryanlei.files.wordpress.com/2011/03/sift_descriptor2.jpg
Scale-Invariant Feature Transform
Invariant to:
• Rotation
• Scale
• Intensity change
• (Some) affine motion
http://ryanlei.files.wordpress.com/2011/03/sift_descriptor2.jpg
Scale-Invariant Feature Transform
RANSAC: Random Sample Consensus
Repeat:
1.Guess minimum
number of points to
determine parameters
2.Check if model works
for other points
http://upload.wikimedia.org/wikipedia/commons/d/de/Fitted_line.svg
Random sampling
https://alliance.seas.upenn.edu/~cis520/wiki/images/cell_phone_face_detection.jpg http://maxcdn.liewcf.com/blog/wp-content/uploads/face-detection-camera-1.jpg
Face detection
http://glaucoma-eye-drops.com/eye.jpg
–
+
-
≥0
–
+
-
≥0
Slightly
better than
random.
http://graphics.stanford.edu/courses/cs148-11-fall/lectures/compression.pdf
Combine weak classifiers
to make a strong one
http://www.codeproject.com/KB/audio-video/haar_detection/features1.png
T (r; c) =
X
i·r;j·c
I(i; j)
?
+1
-1
-1
+1
+1
-1
-1
+1
+1
-1
-1
+1
 Input:
Weak classifiers
hi (~x) 2 f¡1; 1g
 Output:
StrongÃclassifier
! in form
C(~x) = µ
X
i
®i hi (~x)
 Input:
Weak classifiers
hi (~x) 2 f¡1; 1g
 Output:
StrongÃclassifier
! in form
C(~x) = µ
X
i
®i hi (~x)
All sub-windows
Classifier 1
Not a face
50%
Classifier 2
Not a face
…
Classifier 38
Not a face
2%
Face
4916 positive examples, 9544 negative examples
http://www.cs.ubc.ca/~lowe/425/slides/13-ViolaJones.pdf
http://opencv.willowgarage.com/wiki/OpenCVLogo
Context-Based Search for 3D Models
Fisher and Hanrahan 2010
Characterizing Structural Relationships in Scenes Using Graph Kernels
Fisher, Savva, and Hanrahan 2011
Individual
Joint
Joint Shape Segmentation with Linear Programming
Huang, Koltun, and Guibas 2011
A Probabilistic Model of Component-Based Shape Synthesis
Kalogerakis et al. 2012
http://www.cs.tau.ac.il/~wolf/OR/img/street_annotated.jpg
CS 148, Summer 2012
Introduction to Computer Graphics and Imaging
Justin Solomon

Strongly coupled problems - Computer Graphics at Stanford University

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