# Distorted Space

Briefing Distorted Lecture

Additional Reading Chapter 9 in OpenCV 3 Computer Vision with Python Cookbook by Alexey Spizhevoy (author). Search for it in Oria. There is an e-book available.

# Exercises

## An Example of a Distorted space

### Step 1. Preliminaries.

1. Consider a triangle in the object space, formed by the three vertices, $$A=(0,0,10)$$, $$B=(20,0,10)$$, and $$(20,10,10)$$.
Let
• $$\alpha$$ be the angle between the vectors $$\overrightarrow{AB}$$ and $$\overrightarrow{AC}$$.
• $$\beta$$ be the angle between the vectors $$\overrightarrow{BA}$$ and $$\overrightarrow{BC}$$. Draw a figure to show this information.
2. Calculate the angles $$\alpha$$ and $$\beta$$.
3. Consider an ideal perspective camera, posed such that the camera frame matches the world frame.
Calculate the image points corresponding to $$A$$, $$B$$, and $$C$$. Assume that the image plane has $$z=1$$. Draw an image to display this projection.
4. Calculate the angles $$\alpha'$$ and $$\beta'$$ in the image, corresponding to $$\alpha$$ and $$\beta$$. Do they match the original angles?
5. Calculate the lengths of the edges of the triangles in the image.
6. Make a new drawing of the image plane, displaying the quantities calculated in 4 and 5. Keep it for later reference.

Recall the formula for the angle $$\theta$$ between two vectors $$u$$ and $$v$$ $\theta = \cos^{-1}\frac{u\cdot v}{||u||\cdot||v||}.$ This is found in any elementary calculus book. You did not need this above, because the triangles had an orthogonal angle, but you will need it below.

### Step 2. Distortion.

Suppose the camera is not ideal, but instead has calibration matrix $K = \begin{bmatrix} 2 & \frac1{\sqrt{3}} & 4 \\ 0 & 1 & 4 \\ 0 & 0 & 1 \end{bmatrix}$

1. Calculate the image points corresponding to $$A$$, $$B$$, and $$C$$, using the camera calibration matrix $$K$$. (See Ma 2004:55)
2. Draw the resulting image to scale.
3. Calculate the lengths of the edges of the image of the triangle. For now, we use the familiar formulæ for vector norms.
4. Let $$\alpha'$$ and $$\beta'$$ be the images of the angles $$\alpha$$ and $$\beta$$. Calculate these to image angles and add the information to you figure.
5. Compare your distorted image to the original image from Step 1.

### Step 3. The Distorted Inner Product Space

Recall the textbook’s definition of an inner product in distorted space $\langle u,v\rangle_S = u^TSv \quad\text{where } S = K^{-T}K^{-1},$ for some `camera calibration’ matrix $$K$$. We are going to redo Step 2, using this distorted inner product.

1.  Calculate the edges (norms) of the triangle, using the distorted inner product to define the norm.
2.  Calculate the angles $$\alpha'$$ and $$\beta'$$, using the distorted inner product and the formula $\theta = \cos^{-1}\frac{u\cdot v}{||u||\cdot||v||}.$
3. Make a figure illustrating the quantities calculated.
4.  Compare the edges and angles thus calculated with the calculations from Step 2 and from Step 1.

### Step 4. Reflection

Think through the following:

1. What does this exercise tell you about camera distortion and inner product spaces?
2. Is there anything you do not quite understand? Is there something missing in the exercise, something that should have been tested?

• Exercise 6.4

# Debrief

• Step 1
• Step 2(7-10)
• Step 2(11)
• Note how the $$x$$-axis is stretched, doubling the length of $$AB$$.
• Not also how it is skewed, bending the angle $$\beta'$$ from $$90°$$ to $$120°$$.
• Step 3(11)
• Sample python code
• Inverting matrices by hand is usually not time well invested, hence the python code. It is possible though, and for some it may be quicker.