Image Processing Reference
In-Depth Information
The solution to this potential problem is to have a unique threshold value for each
pixel position! Finding these manually, is not realistic simply due to the number
of pixels and the threshold values are therefore found automatically by the use of
the standard deviation for each pixel position, see Appendix C. So when the mean
of each pixel is calculated, so is the standard deviation. Equation 8.3 is therefore
reformulated as
0 ,
if Abs(g(x, y)) < β · σ(x,y) ;
Binary image
255 ,
where β is a scaling factor and σ(x,y) is the standard deviation at the position
(x, y) . Since β is the same for every position, we have no more parameters to define
than above, but now the thresholding is done with respect to the actual data, hence
a local threshold .
Image Differencing
If the assumption of a static background is violated significantly then background
subtraction will produce incorrect results. In such situations we can apply image
differencing to detect changes in a scene. As stated above, image differencing oper-
ates as background subtraction, except for the fact that the reference image is now a
previous image.
Image differencing is simple and can efficiently measure changes in the image.
Unfortunately the method has two problems. The first is a lack of detecting new
objects which are not in motion. Say a new object enters a scene. As long as the
object moves, image differencing detects this in the image subtracting process, but
if the object stops moving, the reference image will be equal to the current image
and hence nothing is detected. This is a clear weakness compared to background
subtraction, which is indifferent to whether the new object is moving or not, as long
as the appearance of the object is different from the background.
The other problem associated with image differencing is the notion of ghost ob-
jects illustrated in Fig. 8.6 . The figure contains artificial images from a sequence
where an object is moving horizontally through a scene. To make it simple, the ob-
ject is a square with uniform gray-scale value. What can be seen is that the image
differencing produces two segments (smaller objects). One originates from the cur-
rent object and the other one from the object in the reference image—where the
object was. This latter segment type is denoted a ghost object, since no object is
present. A ghost object can also be seen in Fig. 8.4 .
If the goal is only to obtain the coarse motion in the image, then this does not
matter. If, however, we are only interested in the position of the object in the current
image, then we need to remove ghost objects. One approach for doing so is if we
know the moving direction of the object. We can then infer which is the object and
which is the ghost. Another approach is if we know that the object is always brighter
than the background. Then the pixels belonging to the ghost will have negative val-
ues after the image subtraction.
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