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Segmentation techniques: Region growing and split and merge
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  • The objective of segmentation is to partition an image into regions. In this section, segmentation is done by finding the regions directly.
  • Let R represent the entire image region segmentation as a process that partitions R into n sub-regions R_1,R_2,…,R_N such that,

    $R_1∪R_2∪…∪R_(N=) R$

    $R_i$ is connected region where i=1,2,…,N

    $R_i∩R_j=∅ for i≠j$

    Predicate $(R_i )$ =True for all i=1,2,..,N

    There are two different approaches for region oriented segmentation.

    1. Region Growing by Pixel Aggregation:
    2. Region growing is a procedure that groups pixels or sub-regions into larger regions.
    3. Pixel aggregation procedure starts with a set of seed point and from these grows region by appending for each seed point those neighboring pixels that have similar proportion.

    4. Region Splitting & Merging:

    5. In this method an image is first subdivided into a set of arbitrary disjointed region and then merges and/or splits the regions.
    6. Let R represent the entire image region and then select a predicate P.
    7. For image one approach for segmenting R is to subdivide it successively into smaller and smaller quadrant region so that for any region Ri, Predicate(Ri) = True.
    8. If Predicate(Region) = False then divide the image into quadrants.
    9. If Predicate(Region) = False for any quadrant then subdivide that quadrant into sub-quadrants and so on.
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