Warning

This page is located in archive.

H = u2H( u, u0 )

Create function `u2H`

computing homography from four image matches. Let `u`

be the image coordinates of points in the first image (2×4 matrix) and `u0`

(2×4 matrix) be the image coordinates of the corresponding points in the second image. Then `H`

is a 3×3 homography matrix, such that

`λ_i [u0(:,i);1] = H * [u(:,i);1]`

for all `i=1:4`

and some nonzero `λ_i`

. Matrix `H`

is regular. Return an empty matrix `[]`

if there is no solution.

- Implement function
`u2H`

. Use the following test data:u = [0 0 1 1; 0 1 1 0] u0 = [1 2 1.5 1; 1 2 0.5 0] % result should be (but verify yourself): H = [1 1 1 -1 1 1 1 0 1 ]

- Download the reference image and your specific image from the upload system, task
`00ID: Your data`

. - Create manually 10 image matches (point correspondences) between the two images. A simple utility
`edit_points`

from the Tools repository can be used (see its help). Store the matches as column vectors in 2×10 matrices`u`

and`u0`

for your and the reference image, respectively. The`i`

-th correspondence is thus`u(:,i)`

and`u0(:,i)`

. - Find the homography (3×3 matrix
`H`

) that maps your image to the reference image. Find it as the best homography by optimizing over all 210 quadruplets among the ten matches. Minimize the maximal transfer error (in the domain of points`u0`

) on all image matches. Create function`u2h_optim`

(with arbitrary inputs and outputs) solving this step. The function will be used in the one of the future homeworks. - Store the matches and the homography as
`05_homography.mat`

save( '05_homography.mat', 'H', 'u', 'u0', 'point_sel', '-v6' )

- Fill the black pixels in your image using the pixels from the reference image and
`H`

. Store the corrected bitmap image as`05_corrected.png`

. Optionally, try some colour normalization of filled-in area (up to**one bonus point**). - Display both images side by side and draw the image matches to both as crosses with labels (1 to 10) and highlight the four points used for computing the best
`H`

. Export as`05_homography.pdf`

.

The part of reference image is transferred to your image, so we need the transformation of coordinates in the opposite direction.

- Generate list of coordinates
`u`

of all pixels in your image that are to be filled. These are typically integers. - Transform these coordinates using the homography. Resulting coordinates
`u0`

point to the reference image (typically not integers). - For each pixel
`i`

:- Look-up the color in the reference image. The easier way is to take the nearest pixel (rounding the coordinates).
rgb = im0(round(u0(2,i)),round(u0(1,i)),:); % here we take all values (':') along the third dimension

In general situations, the coordinates should be checked no to be outside the image. - Fill the color in your image (using corresponding coordinates
`u(:,i)`

).im(u(2,i), u(1,i),:) = rgb;

Upload an archive consisting of:

- u2H.m
- 05_homography.mat
- 05_homography.pdf
- 05_corrected.png
- hw05.m – your Matlab implementation entry point.
- any other files required by hw05.m (including data and files from the repository).

courses/gvg/labs/gvg-2017-hw-05.txt · Last modified: 2018/02/14 15:58 by policmic