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Arithmetic and Geometric Transformations (Chapter 2) CS474/674 – Prof. Bebis.

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1 Arithmetic and Geometric Transformations (Chapter 2) CS474/674 – Prof. Bebis

2 Addition Useful for combining information between two images: 0 <= α <= 1

3 Averaging (see Example 2.5 on page 75) Image quality can be improved by averaging a number of images together (very useful in astronomy applications). 5 10 20 50 100 Note: images must be registered!

4 Subtraction Useful for “change” detection.

5 Subtraction (cont’d) Medical application (iodine medium injected into the bloodstream) difference enhanced

6 Geometric Transformations Transformation applied on the coordinates of the pixels (i.e., relocate pixels). A geometric transformation has the general form (x,y) = T{(v,w)} where (v,w) are the original pixel coordinates and (x,y) are the transformed pixel coordinates.

7 Geometric Transformations affine transformation y=v sinθ + w cosθ

8 Forward mapping Transformed pixel coordinates might not lie within the bounds of the image. Transformed pixel coordinates can be non-integer. There might be no pixels in the input image that map to certain pixel locations in the transformed image No one-to-one correspondence!

9 Forward mapping (cont’) An example of holes due to image rotation, implemented using the forward transformation.

10 Inverse Mapping To guarantee that a value is generated for every pixel in the output image, we must consider each output pixel in turn and use the inverse mapping to determine the position in the input image. To assign intensity values to these locations, we need to use some form of intensity interpolation.

11 Interpolation — process of using known data to estimate unknown values. Interpolation can be used to increase (or decrease) the number of pixels in a digital image. e.g., some digital cameras use interpolation to produce a larger image than the sensor captured or to create digital zoom Interpolation

12 Interpolation (cont’d) Zero-order interpolation: nearest-neighbor

13 Interpolation (cont’d) First-order interpolation: average

14 Interpolation (cont’d) Bilinear interpolation I(x,y) = ax + by + cxy + d The 4 unknowns (a,b,c,d) can be determined from 4 equations formed by the 4 nearest neighbors.

15 Interpolation (cont’d) Bilinear interpolation

16 Interpolation (cont’d) Bicubic interpolation It involves the sixteen nearest neighbors of a point (i.e., 4x4 window). The 16 unknowns a ij can be determined from sixteen equations formed by the 16 nearest neighbors.

17 Examples: Interpolation




21 Image Registration Goal: align two or more images of the same scene. How: estimate a transformation that aligns the two images.

22 Image Registration (cont’d) Under certain assumptions, an affine transformation can be used to align two images. There are 6 unknowns (i.e., t 11, t 12, t 21, t 22, t 31, t 32 ) We need at least 6 equations. Three correspondences are enough, more are better. Unknowns? Equations? Correspondences?

23 Image Registration Example apply affine transformation Error when comparing the transformed and original images.

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