Recently I had been dealing with bugs and cells. I was trying to locate the moving cells Euglena with a camera. The Euglena is a single cell that belongs both to the plans and the animals. A picture of the Euglena is shown below. The length of a single Euglena is about 50 um.
The illumination was not good due to my poor optical setup. I tried to identify the cells according to its intensity and size but neither worked well. The strategy I took at last was extracting the stable background and then compare it with the live video, so that the moving targets can be identified. The way of generating the background is averaging all the grabbed images (or, video as we call them). The changing bits are then smoothed by the number of the frames.
When averaging the images, we assume the mean of N framesimages is A_n and the (N+1)th frame is I_(n+1). Both variables are 2D arrays. Then the mean of (N+1) frames is
A_n+1 = (N * A_n + I_(n+1))/(n+1)
The code is shown below (with re-calculate/ clear function):
This SubVI can be called without external shift registers. This simple function allows us to extract the still background from the video. And thus the moving (or any changing) targets can be extracted no matter how messy the background is. The result is shown in the video below:
As I said in the description of the video, “This demo shows using an algorithm tracing an Euglena in the dish with poor (non-uniform) illumination. The mid-left and mid-right videos are raw videos from the camera. The bottom-left video is the background generated from the video in real-time. The bottom-right video is the target (Euglena) extracted from the video. The top-left video is the coords of the Euglena.” We can see that the Euglena were identified from the video even the illumination is non-uniform and the background is a bit messy.