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!
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.