The implementation calculates the variance of neighboring pixels given a window around a center pixel.
ive used a window size of 3. this means i have a square window of 3x3. the image ive used if the following:
This is the result. ive tried to vary the threshold to get different results. in the bottom image. starting from the top left to right ive used a threshold of 10, 100, 160, 200. The results are very similar to to an adaptive threshold with the exception of it being a negative image. you can see my results on adaptive threshold HERE>>>
Take note that variance is computed for a single image. Just like Background subtraction, it is very sensitive to the threshold and may require trial and error to get good result. however on a cluttered image, it seems very irrational to use this method to identify objects.
I used the same method on a single object on the screen. and i get different results by varying the threshold. Over all it can identify the object very clearly. although setting the right threshold requires alot of trial and error. In the end it has some noise which needs to be removed in the end using morphology.
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