### Success! Joe’s new method.

Success! Although not in implementing Alex’s circle/eye detection method (see last post). I grew tired and frustrated by silly C++, so I decided to make my own, much simpler method that (hopefully) would not require convolutions and FFT’s to fun at a fair speed. It was inspired by the idea of circular wavelets, or circlets, however it is actually nothing to do with them mathematically.

Very simply each pixel considers its own gradient the gradient of the 4 pixels next to it one at a time. The intersection is found between the line made by the gradient of the current pixel, and the line made by the gradient of each adjacent pixel. So we have 4 intersection points. If the intersection points lie on the picture, then we draw a little pyramid of darkness (lets call them Joelets, because its cool!). These pyramids all add up to form some interesting blurry shapes.

How does this detect circles? Well if we are on the edge of a circle, then each gradient will tend to point towards the centre of a circle, so the intersection points will be around the centre. If we are not on the edge of a circle, then the intersection points will just be scattered randomly. Hopefully the little pyramids should all add up near the eyes and cause a massive dark bump. Boom. Eyes detected.

One more issue to consider. How big should the Joelets be? And by big I mean radius wise, as heights are all relative. If they are too big, then everything will just blur together. But if we make them too small, then all we will have is a load of scattered intersection points. I propose that it should be approximately the radius of the eye. I haven’t done any maths to check this, I am only guessing, but it seems to work! That is all well and good, but it relies on us know the eye radius. what if we don’t?? Well we could take the radius of the Joelet to be the distance from the parent pixel to the intersection point. That would be approximately the radius of the circle. It would be brilliant if it was EXTREMELY slow, as some intersection points are on the other side of the image. For now I have just guessed the eye radius, but a task for the future is finding a way to use some kind of variable eye radius. Maybe with a convolution?

Here are my results!