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Geometric Image Transformations

Part One

2D Transformations• Spatial Coordinates

– (x,y) are mapped to new coords (u,v)– pixels of source image -> pixels of destination

image• Types of 2D Transformations

– Affine• Scales• Translations• Rotations• Shears

– Projective• Projections • Homographies or Collineations

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Scale and Translation

• Scale– u = s_x * x– v = s_y * y

• Translation– u = x + t_x– v = y + t_y

Rotation and Shears

• Rotation– u = x*cos θ - y * sin θ– v = x*sin θ + y * cos θ

• Shear– u = x + Sh_x * y– v = y + Sh_y * x

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Shear

• Consider Sh_x only (Sh_y=0)– u = x + Sh_x*y;– v = y;

Homogenous Coords

• Translation is just an addition• We can make it a function of

multiplication by using homogenous coords

xy1

p

=uv1

p’

1 0 tx 0 1 ty 0 0 1

wxwyw

p

=wuwvw

p’

1 0 tx 0 1 ty 0 0 1

Homogenous coords are equivalent up to a scale factor

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Homogenous Coordinates• Transform works on scale (w)

* w

Dividing by the scale “w” puts you in Cartesian coords

w=1

Matrix Notation

a b c d e f 0 0 1

xy1

Affine p

=

uv1

p’

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Matrix notation of transforms

1 0 tx 0 1 ty 0 0 1

xy1

p

=uv1

p’

Sx 0 0 0 Sy 0 0 0 1

xy1

p

=uv1

p’

cosθ -sinθ 0 sinθ cosθ 0

0 0 1

xy1

p

=uv1

p’

1 Shx 0 Shy 1 00 0 1

xy1

p

=uv1

p’

Translation Scale

Rotation Shear

Concatenation

• You can concatenate several transforms into one matrix

A = R S Sh Trotate scale shear translate

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Affine Transformations

Affine is a linear mapping plus a translation:– A function f(x) is linear iff

• f(x+y) = f(x) + f(y)• a*f(x) = f(a*x)

– A function T(x) is Affine if there exists• a linear mapping L(x)• and a constant “c”• Such that: T(X) = L(x) + c (for all x)

Affine Transforms’ Properties

• Preserves parallel lines• Preserves equispaced points along lines

– equally spaced points on a line in the source space

– will produce equally spaced points on a line in the destination space

– (although the scale may be different)• Preserves incident

– Points of intersection hold

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Preserves

Source Destination

Source Destination

6 degrees of freedom

XY1

=uv1

a b cd e f 0 0 1

• We can define an affine transform by specifying 3 point correspondences– 3 (x,y) from the source “space”– that map to 3 (u,v) in the destination “space”

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1

23

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Equations to solve for Affine Transform

• u1 = a*x1 + b*y1 + c• u2 = a*x2 + b*y2 + c• u3 = a*x3 + b*y3 + c

• v1 = d*x1 + e*y1 + f• v2 = d*x2 + e*y2 + f• v3 = d*x3 + e*y3 + f

More commonly

• You can build an Affine transform by concatenating several transforms together– translate to the origin– Rotate by 20 degrees– translate back from the origin– scale by 5

GUI Interface tool that allows you to rotate,translate, scale, etc . .

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Example

Affine Transform Limitation

• Can map a triangle in source space• To a triangle in destination space

– (or two parallelograms)

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Affine Transform Limitation

• Can map a triangle in source space• To a triangle in destination space

– (or two parallelograms)

• What about a rectangle to a general quadrilateral?

Projective Transform

• Projective transform can transform general quadrilaterals between source and destination space

• Does not preserve parallel lines, or lengths

• Does not preserve equispaced points

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Projective transform uses homogenous coords

xy1

=susvs

a b cd e f g h 1

u’ = u/sv’ = v/s

Maps us back to Cartesian space

Projective Transforms• Very common in computer graphics

– Texture mapping a 3D polygon – polygon has been projected onto a plane

Texture map3D perspectiveprojection

3D polygon

2D polygon

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Projective Transform

• Does not preserve length, equispacing

• Does– Map lines to lines– Preserve incidents – Preserve cross ratio

Cross Ratio• Given 4 points on a line in source space and

destination space

A

B

CD

Source Space

A’ B’ C’ D’

Destination Space

|AC||AD||BC||BD|

|A’C’||A’D’||B’C’||B’D’|=

where |XY| is the Euclidean distance between point X and Y

Cross Ratio

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Solving for a Projective Transform

xy1

=susvs

a b cd e f g h 1

8 degrees of freedom

We need 4 point correspondencesbetween source and destination image

su = ax + by + csv = dx + ey + fs = gx + hy + 1

source destination

Remember, with projective transform destination points are:

dest_u = su/sdest_v = sv/s

Solving for a Projective Transformsu = ax + by + csv = dx + ey + fs = gx + hy + 1

u =su = ax + by + cs gx + hy +1

v =sv = dx + ey + fs gx + hy +1

(gx+hy+1)u = ax + by + c (. . . )gx + hy +1

gxu + hyu + u = ax + by + c

u = ax + by + c – gxu – hyu

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8x8 System of Equations

v3h-y3v3-x3v31y3x3000

v2g-y2v2-x2v21y2x200

v1f-y1v1-x1v11y1x1000

u3d-y3u3-x3u30001y3x3

v0e-y0v0-x0v01y0x0000

u2c-y2u2-x2u20001y2x2

u1b-y1u1-x1u10001y1x1

u0a-y0u0-x0u00001y0x0

=

Solve the system

• Matrix is in form:– Ax = b

• Solve for “x”– Gaussian elimination (LU decomposition)– QR decomposition

• Entries of vector “x” are the coefficients for the projective transform

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Properties of Transforms

XXnon-uniform scale

Xcombination (w/ projection)

Xprojection

XXshear

XXXuniform scale

XXXXrotation

XXXXtranslation

Transformation

projectiveaffinesimilarityeuclidean

Properties of Transforms

XXXparallelism

XXXXcross ratio

XXXXincident

XXratio of lengths

XXangle

Xlength

Invariant

projectiveaffinesimilarityeuclidean

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Transforming an Image

Transforms and Images Coordinates

Y

xorigin

f(x,y)

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Image Coords vs. Cartesian Coords

• Image Coords are generally defined by raster alignment– Y [0,Height], X [0,Width]

• Affine, projective transforms are converted into Cartesian coords

Transforms and Images Coordinates

Y

xorigin

f(x,y)

(-y value)

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Cartesian space to image space

• From Cartesian space to Image Space– find (x_min, xmax)– find (y_min, ymax)

– new size dimensions• w = x_max – x_min• h = y_max – y_min• create newImage size (w, h)

– Translate transformed points, such that:• T * (x,y) = (u,v)• newImage( u + abs(x_min), v + abs(y_min) = I(x,y)

Converting to an Image

Y

xorigin

New Image Dimensions

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The transformed image

Y

xorigin New Image Dimensions

Creating the “new” image

• Forward Mapping • Inverse Mapping• Sampling

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Mapping Pixels

(0,0) (0,N)

(0,M) (M,N)

Transform

(0,0) (0,N)

(0,M) (M,N)

1 2

3 4

1

2

3

4

DestinationImage

SourceImage

Forward Mapping

[u,v,s]T = A [x,y,1]T

Forward Mapping

• Draw backs– Source pixels do not map directly to a

single pixel in the destination space

– Possibility for “holes” in the destination image

• We can map the other direction

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black

Reverse Mapping

(0,0) (0,N)

(0,M) (M,N)

1 2

3 4

1

2

3

4

Reverse Mapping

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4

1 2

3 4

[x,y,s]T = A-1[u,v,1]T

“Inverse” Mapping

• Advantages– We assign an “intensity” to each pixel in

the destination (no holes)– Affine/projective transforms have

inverses (not a problem)• just reverse direction of the point

correspondences• We still don’t have pixel to pixel

mapping

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Sampling the source

How do we sample the source to determinethe intensity for the destination?

How do we sample the source to determinethe intensity for the destination?

Sampling the source

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MappingSource

2 x 2 pixels

Option 1 : Pick the pixel nearest to our center.

MappingSource

2 x 2 pixels

Option 1 : Pick the pixel nearest to our center.

Small change results in big difference

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Try different samplingSource

2 x 2 pixelsWhat if we assign an intensityto each vertex and then average?

1 2

3 4

1 2 3 4

Pick the intensity which the vertexlies.

New Sample =

Sampling ExampleSource

2 x 2 pixelsMove the destination slightly.

1 2

3 4

1 2 3 4

Pick the intensity which the vertexlies.

New Sample =

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No DifferenceSource

2 x 2 pixels

1 2

3 4

Source2 x 2 pixels

1

2

3

4

Try different SamplingSource

2 x 2 pixels

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4

What if we had more samples?

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Try different SamplingSource

2 x 2 pixels

1 2

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What if we sampleda larger area?

Try different SamplingSource

2 x 2 pixelsHow should we sample?

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Common Sampling Approaches• Nearest Neighbor

– 1 Sample– Take closest pixel value

• Bi-linear Interpolation– 2x2 (4) Samples– Interpolate from these samples– Slower

• Bi-Cubic– 4x4 (8) samples– Construct a new sample using a non-linear “interpolation”– Slower

Common Sampling Approaches

Bi-Cubic

Bi-linear Interpolation

Nearest Neighbor

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Common Sampling Approaches

Bi-Cubic

Bi-linear Interpolation

Nearest Neighbor

•What do these approaches mean?

•How can evaluate what they are doing?

Sampling and Signal Processing• Proper sampling is a classic “reconstruction problem”• Given a continuous signal f(x)• How do you take discrete samples such that you can properly

reconstruct the signal f(x) from the samples?

f(x)

x