18/03/2010

Why Segmentation?

We use segmentation to divide the image into small regions according to their pixels intensity, that will help us to improve classification performance using segments (little classification data) instead of working pixel by pixel (large amount of data).

And easily to extract the features form the segments.

and if we do classification on the original image we have to work on every pixel and that will take more time than if work on segments , like that :-

classification will be on pixel by pixel (515684 Pixels)

Classification will be on segments (Only 1049 Segments)

you will say that is there is no difference between the 2 images but the first is the original and the 2nd is the segmented image and the colors are similar because we fill the segments with the mean color.

and the algorithm that we use is called flood fill algorithm (Queue Linear Technique) because it’s output is the desired in our problem and it is fast in large image it take seconds to segment it.

Problems That we Faced In Segmentation Phase:-

• Selection of Sigma (difference between colors in one segment):-
  
  1. Large Sigma:- Cause Small number of segments with large regions that may a problem because one segment may contain more than one class and that is a problem when we do classification on it which class that segment will belong to, as shown one segment contain 2 class (Ground , Vertical):-
     

     Large Sigma

     
     

  2. Small Sigma:- Cause Large number of segments (slow the classification process) but this solve 2 big problems:-
     
  • Reduce the probability that 2 class found in one segment.
  • Determine the type of the texture if it coarse or smooth, as show the ground is coarse:-
    

    Coarse Ground

Queue Linear Technique

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