Introduction to Image Processing and Computer Vision...
Transcript of Introduction to Image Processing and Computer Vision...
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Ivo Ihrke / Winter 2013
Introduction to Image Processing and Computer Vision
-- Panoramas and Blending --
Winter 2013/14
Ivo Ihrke
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Ivo Ihrke / Winter 2013
Panoramas
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Ivo Ihrke / Winter 2013
Mosaics and Panoramas
- Outline
- Perspective Panoramas
- Hardware-Based
- Software-Based (Multiple Photographs)
- Image registration
- Image blending
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Ivo Ihrke / Winter 2013
Why Mosaic?
Are you getting the whole picture?
– Compact Camera FOV = 50 x 35°
Slide from Brown & Lowe
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Ivo Ihrke / Winter 2013
Why Mosaic?
Are you getting the whole picture?
– Compact Camera FOV = 50 x 35°
– Human FOV = 200 x 135°
Slide from Brown & Lowe
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Ivo Ihrke / Winter 2013
Why Mosaic?
Are you getting the whole picture?
– Compact Camera FOV = 50 x 35°
– Human FOV = 200 x 135°
– Panoramic Mosaic = 360 x 180°
Slide from Brown & Lowe
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Ivo Ihrke / Winter 2013
Single vs. Multiple Viewpoint
Single-viewpoint
– Necessary for creating pure perspective images.
– Many vision algorithms assume pinhole cameras.
– Images that aren’t perspective images look distorted.
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Ivo Ihrke / Winter 2013
In the old days of film photography
• Single-viewpoint
• Single exposure
• Standard 35mm film
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Ivo Ihrke / Winter 2013
Omnidirectional Catadioptric Cameras
O-360 EyeSee360
catadioptric = mirror + lens system
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Ivo Ihrke / Winter 2013
images: CAVE lab
Images of an Omnidirectional Camera
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Ivo Ihrke / Winter 2013
Catadioptric System – Full Texture
K
[Kuthirummal 2006]
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Ivo Ihrke / Winter 2013
Cata-Fisheye Camera
[Krishnan and Nayhar 2008]
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Ivo Ihrke / Winter 2013
Catadioptric System – Stereo
objectcenter
object’
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Ivo Ihrke / Winter 2013
Multi-camera, Single-viewpoint ?
PointGrey LadybugImmersive Media “Dodeca2000”
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Ivo Ihrke / Winter 2013
Lens image circle “scanning”
Medium format lens
(6 cm x 6 cm
image circle)
Manual Scan plate
APS-C
(2.4 cm x 1.6 cm)
camera
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Ivo Ihrke / Winter 2013
Perspective Panoramas
Registration
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Ivo Ihrke / Winter 2013
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Ivo Ihrke / Winter 2013
Single Center of Projection
– Take a sequence of images from the same position
─ Rotate the camera about its optical center
– Compute transformation between second image and first
– Transform the second image to overlap with the first
– Blend the two together to create a mosaic
– If there are more images, repeat
…why don’t we need the 3D geometry?
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Ivo Ihrke / Winter 2013mosaic PP
Image Reprojection
– The images are reprojected onto a common plane
– The mosaic is formed on this plane
– Mosaic is a synthetic wide-angle camera
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Ivo Ihrke / Winter 2013
A pencil of rays contains all views
real
camerasynthetic
camera
Can generate any synthetic camera view
as long as it has the same center of projection!
scene
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Ivo Ihrke / Winter 2013
No-parallax point
Same center of projection can be ensured by rotating
camera-lens setup around the entrance pupil (and NOT the nodal point!).
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Ivo Ihrke / Winter 2013
Image reprojection
How to relate two images from the samecamera center?
Images contain the same informationalong the same ray.
Use 2D image warp
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Ivo Ihrke / Winter 2013
Taxonomy of Projective Transformations
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Ivo Ihrke / Winter 2013
Perspective Transformation
3D to 2D projection
– Point in world coordinates P(xe ,ye ,ze)
– Distance center of projection – image plane D(=f)
– Image coordinates (xs ,ys)
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Ivo Ihrke / Winter 2013
Homogeneous Coordinates: Point Representation
(x’,y’)
x=(x,y,w)
w=1
y' =y
wx' =
x
w
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Ivo Ihrke / Winter 2013
Homogeneous Coordinates: Point Representation
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Ivo Ihrke / Winter 2013
Hom. Coord.: Line Representation
ax+by+c=0
l=(a,b,c)
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Ivo Ihrke / Winter 2013
Hom. Coordinates: Point on Line
0 lx
l=(a,b,c)
x=(x,y,w)
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Ivo Ihrke / Winter 2013
Hom. Coordinates: Intersection of Lines
xll '
ll’
x
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Ivo Ihrke / Winter 2013
Hom. Coordinates: Line through 2 Points
l=(a,b,c)
x=(x,y,w)
x’=(x’,y’,w’)
lxx '
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Ivo Ihrke / Winter 2013
Embedding of R2 into P2
– For the time being
– Representation of transformations by 3x3 matrices
– Mathematical trick
─ convenient representation to express rotations and translationsas matrix multiplications
─ Easy to find line through points, point-line/line-line intersections
– Easy representation of projective transformation (homography)
Homogeneous Coordinates for 2D
WY
WX
W
Y
X
y
x
y
xR
/
/ and ,P
1
22
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Ivo Ihrke / Winter 2013
Projective Transformations
Projecting one plane onto another using one projection center
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Ivo Ihrke / Winter 2013
Examples of Projective Transformations
• Projection between 2 images via a world plane
Concatenating two projective transforms gives another projective transform
• Projection between 2 images with the same camera center
Rotating camera or camera with varying focal length
• Shadow projection of a plane onto another plane
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Ivo Ihrke / Winter 2013
Taxonomy of Projective Transformations
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Ivo Ihrke / Winter 2013
Taxonomy of Projective Transformations
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Ivo Ihrke / Winter 2013
Distortions under Central Projection
• Similarity: circle remains circle, square remains square
line orientation is preserved
• Affine: circle becomes ellipse, square becomes rhombus
parallel lines remain parallel
• Projective: imaged object size depends on distance from camera
parallel lines converge
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Ivo Ihrke / Winter 2013
Removing Projective Distortion
Projective transformation in inhomogeneous form
4 general point correspondences (x,y ->x’,y’) on the planar facade
lead to eight linear equations of the type
Sufficient to solve for H up to multiplicative factor
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Ivo Ihrke / Winter 2013
The Direct Linear Transform (DLT) Algorithm
Given: 4 2D point correspondences
Objective: estimate the projective transform matrix H
3
2
1
x
x
x
ix
'
'
'
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3
2
1
x
x
x
ix
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Ivo Ihrke / Winter 2013
The DLT Algorithm II
Re-phrasing H
Re-ording into h vector
gives
0
Estimating matrix H from point correspondences is equivalent to
i
i
i
w
y
x
ix
'
'
'
'
i
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hhh
333231
232221
131211
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Ivo Ihrke / Winter 2013
The DLT Algorithm III
Only rows 1 and 2 are linearly independent omit row 3
Inhomogeneous solution: set one matrix entry equal to 1 (e.g. h33)
Solve by Gaussian elimination or least-squares techniques
i
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Ivo Ihrke / Winter 2013
Estimating Homographies
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Ivo Ihrke / Winter 2013
Homography or not ?
• Coincidences between 3D points
at different depths are preserved
• Pure camera rotation
about camera center
2D Homography
• Different depths are imaged to
different image positions
• Camera rotates and translates
Motion Parallax, no Homography
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Ivo Ihrke / Winter 2013
Panoramic Mosaicing
Rotation about camera center: homography
• choose one image as reference
• compute homography to map neighboring
image to reference image plane
• projectively warp image,
add to reference plane
• repeat for all images
bow tie shape
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Ivo Ihrke / Winter 2013
Alternative Panoramas
Project images onto different
surfaces:
Images www.panoguide.com
Cylindrical
Spherical
Cubic (think of cube map)
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Ivo Ihrke / Winter 2013
Example – My former office
Register left and right image to the middle one using two homographies
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Ivo Ihrke / Winter 2013
Example – My former office
all images registered to the central one (2 homographies)
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Ivo Ihrke / Winter 2013
Example – My former office
seams
ghost
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Ivo Ihrke / Winter 2013
Image Blending
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Ivo Ihrke / Winter 2013
« Moyenne » entre deux images
Pas la moyenne de l’image des objets…
…mais une image de la moyenne des objets
et une moyenne évoluant au cours du temps.
Comment savoir ce qu’est la bonne moyenne ?
On n’en sait rien !
Mais les artistes peuvent nous aider
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Linear Blending
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Ivo Ihrke / Winter 2013
Interpolation de l’image complète
It = (1-t) * I1 + t * I2
Mais que se passe-t-il si les images ne sont pas alignées ?
50
Fondu « cross-dissolve »
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Ivo Ihrke / Winter 2013
Aligner puis faire le fondu
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Ivo Ihrke / Winter 2013
Image Blending
slides from Alexei Efros
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Ivo Ihrke / Winter 2013
Feathering
01
01
+
Blending
=
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Ivo Ihrke / Winter 2013
Effect of Window Size
0
1 left
right
0
1
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Ivo Ihrke / Winter 2013
Effect of Window Size
0
1
0
1
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Ivo Ihrke / Winter 2013
Good Window Size
0
1
“Optimal” Window: smooth but not ghosted
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Ivo Ihrke / Winter 2013
What is the Optimal Window?
To avoid seams– window >= size of largest prominent feature
To avoid ghosting– window <= 2*size of smallest prominent feature
Natural to cast this in the Fourier domain– largest frequency <= 2*size of smallest frequency
– do blending in different frequency bands
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Ivo Ihrke / Winter 2013
Bandpass Computations
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Ivo Ihrke / Winter 2013
Bandpass Computations
filtered images
Fourier space filter shape
low-pass 1st octave 2nd octave 3rd octave
Octave = doubling frequency
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Ivo Ihrke / Winter 2013
Lowpass
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Ivo Ihrke / Winter 2013
First Octave
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Ivo Ihrke / Winter 2013
Second Octave
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Ivo Ihrke / Winter 2013
Third Octave
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Ivo Ihrke / Winter 2013
Zoom-In 3rd Octave - Jpeg-Artifacts
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Ivo Ihrke / Winter 2013
Reconstruction
filtered images
Fourier space filter shape
low-pass 1st octave 2nd octave 3rd octave
+ + + =
+ + + =
full freq. range
originallow-pass 1st octave 2nd octave 3rd octave
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Ivo Ihrke / Winter 2013
Spatial Domain Interpretation /Implementation
original
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Ivo Ihrke / Winter 2013
What does blurring take away?
smoothed (5x5 Gaussian)
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Ivo Ihrke / Winter 2013
High-Pass Filter
smoothed minus original
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Ivo Ihrke / Winter 2013
Image Pyramids
mipmap or precursor of wavelets – Gaussian Pyramid
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Ivo Ihrke / Winter 2013
Create by Image Sub-sampling
Throw away every other row and
column to create a 1/2 size image
1/4
1/8
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Ivo Ihrke / Winter 2013
Improper Image Sub-sampling
1/4 (2x zoom) 1/8 (4x zoom)
Why does this look so bad?
1/2
Aliasing!
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Ivo Ihrke / Winter 2013
Proper Sub-Sampling
First, band-limit, then sub-sample !
Repeat
– Filter
– Subsample
Until minimum resolution reached
Whole pyramid is only 4/3 the size of the original image!
filter mask
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Ivo Ihrke / Winter 2013
Implementation by Gaussian pre-filtering
G 1/4
G 1/8
Gaussian 1/2
Filter size should double for each ½ size reduction.
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Ivo Ihrke / Winter 2013
Subsampling with Gaussian pre-filtering
G 1/4 G 1/8Gaussian 1/2Solution: filter the image, then subsample
Filter size should double for each ½ size reduction.
![Page 75: Introduction to Image Processing and Computer Vision ...giana.mmci.uni-saarland.de/website-template/lectures/IntroIPandCV/2013/... · Ivo Ihrke / Winter 2013 Introduction to Image](https://reader031.fdocuments.net/reader031/viewer/2022040422/5e1239fc47ba5b5ec50e2c82/html5/thumbnails/75.jpg)
Ivo Ihrke / Winter 2013
Compare with...
1/4 (2x zoom) 1/8 (4x zoom)1/2
![Page 76: Introduction to Image Processing and Computer Vision ...giana.mmci.uni-saarland.de/website-template/lectures/IntroIPandCV/2013/... · Ivo Ihrke / Winter 2013 Introduction to Image](https://reader031.fdocuments.net/reader031/viewer/2022040422/5e1239fc47ba5b5ec50e2c82/html5/thumbnails/76.jpg)
Ivo Ihrke / Winter 2013
Band-pass filtering
Laplacian Pyramid (subband images) Created from Gaussian pyramid by subtraction
Gaussian Pyramid (low-pass images)
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Ivo Ihrke / Winter 2013
Laplacian Pyramid
How can we reconstruct (collapse) this pyramid into the original image?
Need this!Original image
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Ivo Ihrke / Winter 2013
Laplacian Pyramid
Need this!
![Page 79: Introduction to Image Processing and Computer Vision ...giana.mmci.uni-saarland.de/website-template/lectures/IntroIPandCV/2013/... · Ivo Ihrke / Winter 2013 Introduction to Image](https://reader031.fdocuments.net/reader031/viewer/2022040422/5e1239fc47ba5b5ec50e2c82/html5/thumbnails/79.jpg)
Ivo Ihrke / Winter 2013
Pyramid Blending
0
1
0
1
0
1
Left pyramid Right pyramidblend
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Ivo Ihrke / Winter 2013
Blending Apples and Oranges
original apple original orange
blend scale 1 blend scale 2 blend scale 3 pyramid blending
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Ivo Ihrke / Winter 2013
Blending Apples and Oranges
blend scale 1 pyramid blending
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Ivo Ihrke / Winter 2013
Blending Apples and Oranges
blend scale 2 pyramid blending
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Ivo Ihrke / Winter 2013
Blending Apples and Oranges
blend scale 3 pyramid blending
![Page 84: Introduction to Image Processing and Computer Vision ...giana.mmci.uni-saarland.de/website-template/lectures/IntroIPandCV/2013/... · Ivo Ihrke / Winter 2013 Introduction to Image](https://reader031.fdocuments.net/reader031/viewer/2022040422/5e1239fc47ba5b5ec50e2c82/html5/thumbnails/84.jpg)
Ivo Ihrke / Winter 2013
Different Frequency Bands
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Ivo Ihrke / Winter 2013
Simplification: Two-band Blending
Brown & Lowe, 2003
– Only use two bands: high freq. and low freq.
– Blends low freq. smoothly
– Blend high freq. with no smoothing: use binary mask
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Ivo Ihrke / Winter 2013
Low frequency (l > 2 pixels)
High frequency (l < 2 pixels)
2-band Blending
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Ivo Ihrke / Winter 2013
Linear Blending
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Ivo Ihrke / Winter 2013
2-band Blending
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Ivo Ihrke / Winter 2013
Still Some Artifacts Left…
Ghosting—objects move in the scene.
Differing exposures between images.
– Pyramid blending does not solve this.
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Ivo Ihrke / Winter 2013
De-Ghosting
In regions with differences don’tblend - crop.
[Uyttendaele et al. 2001]
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Ivo Ihrke / Winter 2013
Gradient Domain Blending
In Pyramid Blending, we decomposed our image into 2nd derivatives (Laplacian) and a low-res image
Let us now look at 1st derivatives (gradients):
No need for low-res image
– captures everything (up to a constant)
– easy to deal with low-frequency differences
Idea:
– Differentiate
– Blend
– Reintegrate
![Page 92: Introduction to Image Processing and Computer Vision ...giana.mmci.uni-saarland.de/website-template/lectures/IntroIPandCV/2013/... · Ivo Ihrke / Winter 2013 Introduction to Image](https://reader031.fdocuments.net/reader031/viewer/2022040422/5e1239fc47ba5b5ec50e2c82/html5/thumbnails/92.jpg)
Ivo Ihrke / Winter 2013
Poisson Image Editing
original mask
Poisson Inpainting result
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Ivo Ihrke / Winter 2013
Poisson Image Editing
original
copy and paste
original to paste
Poisson Image Editing result
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Ivo Ihrke / Winter 2013
Gradient Domain Blending (2D)
– Take partial derivatives dx and dy (the gradient field)
– Fiddle around with them (copy, smooth, blend, feather, etc)
– Reintegrate
─ But now integral(dx) might not equal integral(dy)
– Find the most agreeable solution
─ Equivalent to solving Poisson equation
![Page 95: Introduction to Image Processing and Computer Vision ...giana.mmci.uni-saarland.de/website-template/lectures/IntroIPandCV/2013/... · Ivo Ihrke / Winter 2013 Introduction to Image](https://reader031.fdocuments.net/reader031/viewer/2022040422/5e1239fc47ba5b5ec50e2c82/html5/thumbnails/95.jpg)
Ivo Ihrke / Winter 2013
Gradient Domain Blending (2D)
- But now integral(dx) might not equal integral(dy):
INCONSISTENCY
- There is no UNIQUE SOLUTON!
- Poisson-solver (most widely used) can produce artifacts.
This is how it looks like when we directly integrate an inconsistent gradient field (row-by-row in this case)
+10 ¹ +70
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Ivo Ihrke / Winter 2013
Comparisons [Levin et al 2004]
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Ivo Ihrke / Winter 2013
End
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Ivo Ihrke / Winter 2013
Acknowledgements
Many slides by Steve Seitz, Rick Szeliski
– http://szeliski.org/book
Histogram slides by Samir H. Abdul-Jauwad
Some histogram-matching results by Paul Bourke
More slides by Pierre Bénard, HendrikLensch
![Page 99: Introduction to Image Processing and Computer Vision ...giana.mmci.uni-saarland.de/website-template/lectures/IntroIPandCV/2013/... · Ivo Ihrke / Winter 2013 Introduction to Image](https://reader031.fdocuments.net/reader031/viewer/2022040422/5e1239fc47ba5b5ec50e2c82/html5/thumbnails/99.jpg)
Ivo Ihrke / Winter 2013
The End