Individual pixels in an image are marked as “object” pixels if their value is greater than some threshold value and as “background” pixels otherwise → threshold. Abstract. Region-based image segmentation techniques make use of similarity in intensity, color and texture to determine the partitioning of an image. The fourth step is the region growing stage, which segments the image into regions using the seeds generated. Finally, a region-merging algorithm is applied to merge similar regions to avoid over-segmentation. Overview of the Region-based Image Segmentation Algorithm based on k-means clustering (RISA).


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If we make the range of threshold wider, we will get a result having a bigger area of the lightning region shown as the Figure 4 and the Figure 5.

We can observe the difference between the last two figures which have different threshold values. Region growing provides the region based image segmentation for us to separate the part we want connected.

As we can see in Figure 3 to Figure 5, the segmented results in this example are seed-oriented connected. That means the region based image segmentation grew from the same seed points are the same regions.

And the points will not be grown without being connected with the seed points. Therefore, there are still lots of points in the original image having the grayscale values above which are not marked in Figure 5.

This characteristic ensures the region based image segmentation of the segmentation and provides the ability to resist noise. For this example, this characteristic prevents us marking out region based image segmentation non-lightning part in the image because the lightning is always connected as one part.

The advantages and disadvantages of region growing[ edit ] We briefly conclude the advantages and disadvantages of region growing.

Lecture 2: Region Based Segmentation

Region growing methods can correctly separate the regions that have the same properties we define. Region region based image segmentation methods can provide the original images which have clear edges with good segmentation results.

The concept is simple. We only need a small number of seed points to represent the property we want, then grow the region.

We can determine the seed points and the criteria we want to make. We can choose the multiple criteria at the same time. It is a local method with no global view of the problem. Unless the image has had region based image segmentation threshold function applied to it, a continuous path of points related to colour may exist which connects any two points in the image.

Region growing - Wikipedia

The usual criterion for stopping the splitting process is when the properties of a newly split pair do not differ from those of the original region by more than a threshold. The chief problem with this type of algorithm is the difficulty of deciding where to make the partition.

Quadtrees for Region Extraction An important data structure which is used in split and merge algorithms is the quadtree. Note that in graphics the quadtree is used in a region splitting algorithm Warnock's Algorithm which breaks a graphical image down region based image segmentation from the root node, which represents the whole image, to the leaf nodes which each represent a coherent region, which can be rendered without further hidden line elimination region based image segmentation.

The same use is made of quadtrees for vision. Quadtrees impose one type of regular decomposition onto an image. To complete the segmentation process this must be followed by a merging phase.


Thus the problem of finding adjacent neighbours to a given node has been studied Diagram 2. The problem is one of tree search and efficient algorithms have been published.