last figure we can see that the water almost fills the dams out, until the highest level of the gray level in the images researched. The final dam corresponds to the watershed lines which are the desired segmentation result. The principle applications of the method are in the extraction of uniform objects from the background. Regions are characterized by small variations in gray levels, have small gradient values. So it is applied to the gradient than the image. Region with minimum correlated with the small value of gradient corresponding to the objects of interest. Use of Motion in segmentation: Motion of objects can be very important tool to exploit when the background detail is irrelevant. This technique is very common in sensing applications. Let us consider two image frames at time t1 and t2, f(x,y,t1) and f(x,y,t2) and compare them pixel to pixel. One method to compare is to take the difference of the pixels D12(x,y) = 1 if | f(x,y,t1) f(x,y,t2)| > T, – = 0 otherwise. Where T is a threshold value.
last figure we can see that the water almost fills the dams out, until the highest level of the gray level in the images researched. The final dam corresponds to the watershed lines which are the desired segmentation result. The principle applications of the method are in the extraction of uniform objects from the background. Regions are characterized by small variations in gray levels, have small gradient values. So it is applied to the gradient than the image. Region with minimum correlated with the small value of gradient corresponding to the objects of interest. Use of Motion in segmentation: Motion of objects can be very important tool to exploit when the background detail is irrelevant. This technique is very common in sensing applications. Let us consider two image frames at time t1 and t2, f(x,y,t1) and f(x,y,t2) and compare them pixel to pixel. One method to compare is to take the difference of the pixels D12(x,y) = 1 if | f(x,y,t1) – f(x,y,t2)| > T, = 0 otherwise. Where T is a threshold value.
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