AI Fun

Joe's blog about AI things!

Tag: academic

Tying up strings.

Hello world! It has been a while, due to me finishing the work experience I was doing during the summer of 2012.

I recently received news that the paper I was working on has finally be published!

If you go to, and then ctrl+f search for “Explore New Eye Tracking and Gaze Locating Methods”then boom.

Thankyou, and goodnight!

A New and Exciting Method. The Isophote.

Some few of you may recall one of my previous posts (Success! Joe’s new method.) I outlined and implemented a technique to detect circles. My idea was to consider pairs of adjacent pixels, and calculate where their gradients intersected, thus giving an approximate centre for any dark circles they lay on. Well, I was reading a paper recently, and it outlined a very similar method, but instead of considering two pixels, you considered only one (something that I had assumed was possible, but had no idea how to do).

First the paper introduced the concept of an isophote. This is simply an area of constant darkness/intensity. So if our image is described by a function f(x,y), then we have an isophote at (x,y) where f(x,y) = f0 (f0 is constant). This is essentially describing contours. It then goes on to use the curvature of each pixel, assuming it is on an isophote, to estimate the radius of the circle it is on. Curvature is the second derivative of y with respect to x, and a formula for this can be derived from the definition of an isophote using total derivatives (as f = a constant, we have y(x), so we must use the total derivative, paying attention to the chain rule). The radius of the circle that it is on is given by 1/curvature, which is very convenient. To convince yourself of this, try plugging the values x = 0, y = r into the definition of curvature, and you should get 1/r. Easy. So now we have the distance to the centre. The direction to the centre is given by the gradient at that point. All it takes is a little trigonometry, and we have an estimate!

So here is a pretty picture of it at work:

Read the rest of this entry »

The slowness of a Joelet.

Remember before when I commented on how slow it would be to draw a large “bump” onto an image at every estimate? Well I did even if you don’t. It seems that Gaussian blur (again discussed in an earlier post) offers a perfect solution. If I draw every estimate on as a single dot, and then blur nearby dots into eachother, then Boom! Estimates are grouped in a fast and efficient manner.

I have also altered the shape of the Gaussian function to more of an oval shape to accommodate eye shapes. This could be good, this could be bad. I have not tested it yet. The following is using an oval shape. I have also noticed that the picture gets darker with more spread out Gaussian functions…. I think I need to moderate the height so as to have a constant area under the function. Integration awaits me!

Original image:

And now after processing, the left is using the original “Joelet” method, the right is using Gaussian blur:


A little different, but I think the average number of eyes detected would be the same.

Great! This would make detecting bigger eyes much more efficient. Although I am still relying on the user inputting an approximate eye size, when I find a fairly optimum Gaussian function shape I think I could probably do away with that.


Alex’s method 3, and some tasty paper.

Just thought I would share a little failure with regard to Alex’s method. I just tried to implement it using FFT to solve some convolutions, which is a LOT faster – even without me trying to streamline it. It does, however, result in every pixel having a negative colour. Which is bad. Very bad.

The worst part is that I’ve seen *I MY CHAIR JUST DISINTEGRATED UNDERNEATH ME!!* Alex’s method implemented in matlab code, but I such a matlab noob, I have a lot of trouble following it. In the near future I might just sit down and try extremely hard to make it work, using the matlab code and everything.

In other news, I have been trying to do a literature review on circle finding techniques (my first ever lit review!). It seems to me there are a few main techniques.

  1. Hough Transform. This is originally used for detecting straight lines, but has been modified to detect any number of shapes, including circles.
  2. Wavelets. Wavelets are the basis for how jpegs works, and are very powerful tools for looking at a picture in different resolutions. In particular, there have been modifications to “circlets” which can be used to track down circles.
  3. Other, misc. Lots of them similar to mine, in which some estimate is made of the centre of each pixel or group of pixels.

I also read n interesting paper about the relationship between head pose and eye gaze, and in particular, how to tell if someone is surprised by something they looked at, or whether they always intended to look at it. Essentially it showed that if a person looks with their eyes, and then turns their head, they are startled. But if they move their head a bit before their eyes, then they intended to look over there.

Fascinating stuff.