# Visualizing the floating-point behavior of the point-on-line test 1

Posted by **Steve Eddins**,

Today's post is about this picture, which is from the paper "Classroom Examples of Robustness Problems in Geometric Computations," by L. Kettner, K. Mehlhorn, S. Pion, S. Schirra, and C. Yap, *Computational Geometry*, vol. 40, n. 1, May 2008, pp. 61-78.

Yes, I know, I was supposed to do another follow-up on my Cody problem of eliminating unnecessary polygon vertices. But I really am following up on the Cody problem, if a bit indirectly.

I was looking at solution 108897 by Cris, and I paused over these lines:

```
v1 = p2-p1;
v2 = p3-p2;
if ( v1(1)*v2(2) == v1(2)*v2(1) ) && any(v1.*v2>0)
```

I recognized the expression `( v1(1)*v2(2) == v1(2)*v2(1) )` as a way to see if the point `p2` is on the line segment formed by `p1` and `p3`. It reminded me of Loren's 06-Jun-2008 blog post on methods for determining collinearity, and it also reminded me of the paper above.

I decided to see if I could reproduce the figure in MATLAB.

The figure concerns the behavior of floating-point methods for computing the *planar orientation predicate*. Given three points $p$, $q$, and $r$ in a plane, does the point $r$ lie to the left of the line through $p$ and $q$, to the right of that line, or on the line? One way to compute this is via the equation

$$O(p,q,r) = \mbox{sign}((q_x - p_x)(r_y - p_y) - (q_y - p_y)(r_x - p_x))$$

The figure in the paper represents an experiment using $q=(12,12)$, $r=(24,24)$, and $256^2$ different locations for the point $p$, all of which are *extremely* close to the point $p=(0.5,0.5)$. More specifically, the figure is a visualization of $O((p_x+xu, p_y+yu),q,r)$ for $0 \leq x,y \leq 255$ and $u=2^{-53}$. This value of $u$ is the increment between adjacent double-precision floating-point numbers in the interval $0.5 < x \leq 1$.

Here's a straightforward implementation of the orientation equation above.

```
function out = floatOrient(p,q,r)
```

```
out = sign((q(1) - p(1))*(r(2) - p(2)) - ...
(q(2) - p(2))*(r(1) - p(1)));
```

Let's see what we get when we compute the orientation for all those different point locations $p'$ that are extremely close to $(0.5,0.5)$.

[m,n] = meshgrid(0:255); Px = 0.5 + n*eps(0.5); Py = 0.5 + m*eps(0.5); q = [12 12]; r = [24 24]; out = zeros(size(Px)); for u = 1:256 for v = 1:256 out(u,v) = floatOrient([Px(u,v), Py(u,v)], q, r); end end

(Yes, I know ... `floatOrient` and the calling code above can all be vectorized.)

I'll show in yellow the locations $p'$ where $r$ has been determined to be on the line containing $p'$ and $q$. I'll use blue and red to show where $r$ has been determined to lie on one side or the other of the line.

image(out + 2); axis equal; set(gca,'ydir','normal') axis off colormap([0 0 1; 1 1 0; 1 0 0]) hold on plot([.5 256.5],[.5 256.6],'k','LineWidth',3) hold off

How do we interpret this? First, I want to emphasize that this matrix represents the results for $p'$ that are all *extremely* close to $(0.5,0.5)$. The lower left corner corresponds exactly to the $(0.5,0.5)$, while the upper right corner corresponds to approximately $(0.500000000000028,0.500000000000028)$. Ideally, only the results lying along the black line *should* be yellow; these are the locations where $p'$, $q$, and $r$ are collinear. Our floating-point computation method is showing many other locations in yellow. Notice also that a few pairs of locations $p'$ are actually being computed *reversed*. A left turn has been determined to be a right turn, and vice versa.

In the comment thread on Loren's collinearity post, Tim Davis suggested using a rank test to determine collinearity. It looks something like this:

```
function out = rankCollinear(p,q,r)
out = rank([p-q ; q-r]) < 2;
```

Let's see how this test does on our collection of points.

for u = 1:256 for v = 1:256 out(u,v) = rankCollinear([Px(u,v), Py(u,v)], q, r); end end image(out + 1); axis equal; set(gca,'ydir','normal') axis off colormap([0 0 0; 1 1 0]) hold on plot([.5 256.5],[.5 256.6],'k','LineWidth',3) hold off

We only see yellow pixels, not red and blue, because `rankCollinear` only checks for collinearity, not the left or right turning of noncollinear points. The width of the section of yellow pixels depends on the numerical tolerance used by the `rank` function.

I am fascinated by this particular experiment and visualization. It reminds me once again that seemingly straightforward geometric ideas can be devilishly difficult to implement with a computer program.

Get the MATLAB code

Published with MATLAB® 7.14

### Note

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## 1 CommentsOldest to Newest

**1**of 1

You get slightly different results with the determinant function:

function out = floatOrient(p,q,r)

out = sign( det([q-p;r-p]) );

end

I had something similar in my solution as well:

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