Robust Tracking Of Blobs

I have an image feature extraction problem. The input images are binary (black and white) and may contain blobs of approximately known area and aspect ratio. These need to be fit

Solution 1:

I would try to fit a gaussian mixture model and then use the mean and the covariance matrix to fit ellipses over the data. Such a model would work even with overlap and small noisy blobs. The data which you have would be the the coordinates of the pixels which are black and you could fit a GMM over the data. One issue with this approach is that you need to know the number of blobs you need to track in advance, if you can come up with a heuristic for that, GMM should solve this problem pretty efficiently.

Solution 2:

You fill the holes [1], you detect contours [2], and use moments on each contour rectangle to find orientation, eccentricity, etc[3].

PS.: The disconnected contours (noise) can be filtered out by size.

Solution 3:

You might start by filtering out contours by area.

About separating overlapping blobs, that might be a tricky one (I would risk to say, impossible for arbitrary overlaps) to do with this binary image, maybe you should do it with the original image or at least some steps back of pre-processing.

OpenCV's fittEllipse will also be helpful.

Solution 4:

this is not some basic programming problem, this involves advanced image processing techniques. from what I know, "Image Processing, morphology" are the points you are targeting for. you can take some course in "Image Morphology" understand those basic constructs such as "Dilation, Erosion" etc... then you have the fundamentals solving this issue at hand.

Solution 5:

Since you have the size and orientation, you can draw each ellipse, and use template matching.

see tutorial here.