Activity 12: Color Image Segmentation

For this activity, we separated a particular color in an image. If a color of an object in an image is uniform (i.e. no shading variations), it is relatively much easier to separate that particular object. However, this becomes difficult when dealing images of three dimensional objects because of shading variations. To do this, we must indicate a "range" of colors that are present in an object (or more properly called as region of interest). To methods are presented here: parametric and non-parametric. Parametric approach uses a function to specify the probability of a color to be present in the ROI. The parameters of the function are computed from a patch of the ROI. Non-parametric involves counting of the colors present in the ROI patch and the histogram of which is taken as the probability distribution. The quality of segmentation depends on the size of the bins used in the histogram. The results are as follows

Figure 1. Left: Original image. Middle: Parametric segmentation showing only orange objects. Right: Parametric segmentation showing only red objects.



Figure 2. Non-parametric segmentation with varying bin sizes 10, 32, 64 from left to right. Top row: Segmentation for orange objects. Bottom row: Segmentation for red objects.

From figure 1 and 2, it can be noticed that the segmentation is not perfect. This is because of the limited size of the patch that used and also some of the objects in the image has some of the colors of the ROI.

For this activity, I'll give myself a 9. I have to admit that this was done in a hurry. I still have lots of images but for reason of time conservation I opted to post just a few.