Green Noise

When the ball is far away from the robot the ball's image is very small and only turns on a couple pixels. With so few pixels the RGBFilter becomes more sensitive to background noise. This problem is further exacerbated when the image size is reduced to a small 160x120. Because of this additional noise the COG module starts to have issues centering on the actual ball and may chose not to target the ball at all. This problem can be seen in the following images:

   

You can see that the processed image using the green RGBFilter introduces a lot of noise. Looking closely at the original image we notice some spurious green pixels that are somewhat isolated in parts of the image that are not actually green but form the transition from one color to another. We think this noise is introduced by the NTSC camera to digitizer process on the BucketBot as we do not see those pixel transition problems on other web cameras.

Regardless, to reduce this error we introduce a mean filter of size 12 to the processing stream. Huh?? The mean filter causes the image to become blurred. This blurring will reduce the spikiness of the spurious pixels and allow the RGBFilter to better focus on the actual ball! This blurring trick does have some correlation to the human visual system. It is theorized that the gaussian blur is an active component of our visual system. While not quite a gaussian distribution the mean or box filter provides a similar effect at a much faster frame rate. And you thought you needed glasses to see those precise edges?

   

Now that we can better track the ball even at distance we now have to understand when and how to move.