Warning

This page is located in archive.
Go to the latest version of this course pages.
Go the latest version of this page.

This is a simple task that demonstrates working with homogeneous planar points and lines.

- Let the image area has an extent
`[1, 1]`

to`[800, 600]`

. Draw its boundary. - Develop a simple tool, allows to enter two pairs of points within this area and displays them.
- Calculate the straight line passing through the first pair and the straight line passing through the second pair. Use homogeneous representation. Display the intersection of each line with the image area.
- Calculate the intersection of both lines and draw it, if it is inside the image area.
- Apply following homography to all entities and draw the result to another figure.

H = [1 0.1 0; 0.1 1 0; 0.004 0.002 1 ];

Example result of this task is shown in figure 1.

Notes: the Matlab function `ginput`

is suitable for entering the points.

[u v] = ginput( 4 ); % or x = ginput(4)'; % note that ginput returns row vectors, hence the transpose

Develop a simulation of perspective camera projection – wire-frame model. Let the 3D object be given. The object is composed from two planar diagrams, that are connected. Coordinates of vertices of both diagrams `X1`

and `X2`

are:

z = 4; X1 = [-0.5 0.5 0.5 -0.5 -0.5 -0.3 -0.3 -0.2 -0.2 0 0.5; -0.5 -0.5 0.5 0.5 -0.5 -0.7 -0.9 -0.9 -0.8 -1 -0.5; z z z z z z z z z z z ]; X2 = X1; X2(3,:) = X1(3,:)+0.5;

Wire-frame model contains edges, that connects vertices in `X1`

and `X2`

in given order, and additionally it contains edges connecting vertices between `X1`

and `X2`

, such that the vertex `X1(:,i)`

is connected to the vertex `X2(:,i)`

, ∀ `i`

.

The internal calibration matrix of the camera is:

K = [ 1000 0 500; 0 1000 500; 0 0 1 ];

- Construct following camera matrices (keep the image
*u*-axis parallel to the scene*x*-axis):`P1`

: camera in the origin looking in the direction of*z*-axis.`P2`

: camera located at`[0;-1;0]`

looking in the direction of*z*-axis.`P3`

: camera located at`[0;0.5;0]`

looking in the direction of*z*-axis.`P4`

: camera located at`[0;-3;0.5]`

, with optical axis rotated by 0.5 rad around*x*-axis towards*y*-axis.`P5`

: camera located at`[0;-5;4.2]`

looking in the direction of*y*-axis.`P6`

: camera located at`[-1.5;-3;1.5]`

, with optical axis rotated by 0.5 rad around*y*-axis towards*x*-axis (i.e.,**-**0.5 rad) followed by a rotation by 0.8 rad around*x*-axis towards*y*-axis. See figure 3.

- Use the cameras
`P1`

to`P6`

for projection of given wire-frame model into an image. The edges inside`X1`

should be drawn red, the edges inside`X2`

should be drawn blue and the rest should be drawn in black.

The example projection by the camera `P1`

is shown in figure 2.

**Drawing**: let `u1 = [ u1_1, u1_2, …]`

, `v1`

be the image coordinates of projected vertices `X1`

and `u2`

, `v2`

be the coordinates of projected vertices `X2`

. The desired picture can be drawn, e.g., like this:

plot( u1, v1, 'r-', 'linewidth', 2 ) hold on plot( u2, v2, 'b-', 'linewidth', 2 ) plot( [u1; u2], [v1; v2], 'k-', 'linewidth', 2 ); set( gca, 'ydir', 'reverse' ) axis equal

See the probabilistic model of line and model fitting.

- Let the image area has an extent [1, 1] to [800, 600].
- Chose a planar line in general position, suitably inside the image.
- Randomly generate a set of 800 points belonging to the line. The points are polluted by an isotropic gaussian noise with σ=3 [px]
- Randomly generate an additional set of 200 outlying points, uniformly distributed over the image area.
- Mix all the points together.
- Use the set of all points for (non-robust) ML estimation of the line. Use numeric optimisation (
`fminsearch)`

). This is expected to fail. - Use the set of all points for robust ML estimation of the line. Use RANSAC to separate inliers and outliersf and than numeric optimisation using the inliers.
- Plot the whole situation:
- Original line.
- Points.
- Line found by non-robust ML estimations.
- Line found by RANSAC (prior to optimisation)
- Line found by robust ML estimation (RANSAC followed by non-robust optimisation).

Consider a pair of images of a scene with two dominant planes, e.g. a pair of images from a triple below. Each plane generate one homography between the image pair. i.e. there are two homographies **Ha** and **Hb**.

When a set of tentative correspondences between the images is known (see 2_sparse_correspondences), these homographies can be estimated. Additionally, since these two homographies are generated by two scene planes, there is a line in both images, that is a projection of the intersecting line of the planes. The image points **u1**, **u2** laying on that line in both images are related by both homographies, i.e., **u2** ∼ **Ha** **u1** ∼ **Hb** **u1**, which can be written as inv(**Hb**) **Ha** **u1** = λ **u1**. Thus these two homographies must be estimated such that these constraint is enforced (two arbitrary regular 3×3 matrices generally do not ensure the existence of such a common line. Note that this constraint means that there exist a common line that is transformed same way by the two complementary **line homographies** inv(**Ha**') and inv(**Hb**') (this line always exists for any two regular 3×3 matrices) but additionally that this line is transformed by both homographies from the first image to the same line in the second one **point-wise**.

There are many methods how to estimate multiple geometric models (homographies in this case) from given data. Two simplest possibilities are:

- Sequential RANSAC
- Estimate the first homography using standard RANSAC scheme for a single homography. Typically the most dominant one is found.
- Remove inliers of the first found homography from the data.
- Estimate the second homography using the remaining data, such that the common-line-constraint is enforced (i.e., in each sample, a hypothesis that violates the constraint is not accepted).

- Multi RANSAC with conditional sampling
- Estimate both homographies simultaneously in RANSAC scheme by sampling 8-tuple of correspondences.
- In every sample:
- Draw 4 random correspondences and estimate
**Ha** - Remove inliers of
**Ha**from the data set - Draw 4 random correspondences and estimate
**Hb** - Verify common-line-constraint, reject the hypothesis if violated
- Verify support of both homographies, accept the best one

Note that having two homographies an inlier must be decided to which it belongs to. This can be done based on minimal error or according to on which side of common line the point is laying.

- Select at least one pair of images from the set above. Alternatively, bring your own images with two dominant planes. Note that in the set above, the pair 1-2 is the easiest one and the pair 1-3 is the hardest one considering the stability of solution.
- Use WBS to obtain set of tentative correspondences.
- Estimate the two dominant homographies by a method of your choice.
- Estimate the common line.
- Show the results - show in both images outliers and inliers of either homography (in different colours) and the common line. Show the correspondences as needle map. See example in figure 4.

courses/tdv/labs/0_geometry.txt · Last modified: 2018/10/23 12:49 by xmatousm