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Find your data in the assignment '00data: Your data' in the submission system; the image `daliborka_01.jpg`

, coordinates of 3D points `x`

and their projections `u`

, and point index vector `ix`

. The data can be loaded e.g.

load( 'daliborka_01-ux.mat' ); % loads all variables from the file into the workspace img = imread( 'daliborka_01.jpg' );

- Load the points
`u`

,`x`

and the image into your matlab workspace. - Examine the image points
`u`

by displaying them over the imageimage( img ); hold on; % without this, the next drawing command would clear the figure plot( u(1,:), u(2,:), '.' ); hold off axis equal

- Examine the 3D points
`x`

by displaying them (into a new figure created by e.g.`subfig`

) (The 3D plot can be e.g. rotated.)plot3( x(1,:), x(2,:), x(3,:) ) axis equal

- Implement the estimation of camera projection matrix
`Q`

from given image points`u`

and 3D points`x`

given your selection index`ix`

as a function[Q points_sel err_max err_points Q_all] = estimate_Q( u, x, ix )

where`Q`

is the best projection matrix,`points_sel`

are indices of the 6 points (w.r.t to all 109 points). The other output arguments are optional (not tested by A.E.), for your convenience:`err_max`

should be vector of all maximal errors for all tested matrices,`err_points`

should be vector of point errors for the best camera and`Q_all`

should be cell matrix containing all tested camera matrices (will be used in HW-03).- Perform all possible selections of 5 1/2 points from your 10 points (using
`ix`

) - For each selection compute the projection matrix
`Q`

projecting the selected 5 1/2 points exactly. - Compute the reprojection errors – Euclidean distances between measured image points
`u`

and the projections of 3D points`x`

using the particular matrix`Q`

(for all 109 points). Find the maximum error over all the correspondences. - From all computed projection matrices select the one that has the maximum reprojection error minimal.

- Plot the decadic logarithm (
`log10()`

) of the maximum reprojection error of all the computed projection matrices as the function of their selection index and export the plot as a pdf file`02_Q_maxerr.pdf`

. (Use`fig2pdf.m`

utility in Tools repository.) - Display the image and plot
`u`

as blue dots (plot specifier`'b.`

'), highlight the points used for computing the best`Q`

by plotting them as yellow dots (`'y.`

'), and plot the projections of`x`

using the best`Q`

as red circles (`'ro`

'). Export the plot as`02_Q_projections.pdf`

. - Display the image and plot
`u`

as blue dots, highlight the points used for computing the best`Q`

by plotting them as yellow dots, and plot the displacements of projected points`x`

multiplied 100 times as red lines. Export the plot as`02_Q_projections_errors.pdf`

. - Plot the reprojection error of the best
`Q`

on all 109 points as the function of point index and export as`02_Q_pointerr.pdf`

.

(Note: do not forget to create figure titles and describe axes where appropriate.)

Upload an archive containing the following files:

`02_Q_projections.pdf`

,`02_Q_projections_errors.pdf`

`02_Q_maxerr.pdf`

,`02_Q_pointerr.pdf`

`estimate_Q.m`

- implementation of the P matrix estimation`hw02.m`

- your Matlab implementation. It makes all required figures, output files and prints.- any other files required by hw02.m.

Note: The required files must be in the root directory of the archive.

courses/gvg/labs/gvg-2017-hw-02.txt · Last modified: 2019/01/19 18:22 (external edit)