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feature extraction step. Conventional classification techniques are grouped in two categories: supervised an
unsupervised techniques. In a supervised method, classifiers learn with the help of training sets but in the case of
an unsupervised method, classifiers learns without training sets.
During the man-made object extraction, there will be a lot of small unwanted regions after segmentation made.
Some of the regions can be noise and some other are due to the unimportant effects that must be removed from
the segmented image. In this paper, implementation of split and merge method for segmentation of image has been
made, then morphological operations for image simplification have been investigated.
2 IMAGE SEGMENTATION
The objective of segmentation is to split an image into regions with similar properties. The grade of similarity is
evaluated for each region by a homogeneity criterion.
It is required that the segmentation to be complete and segmented regions to be disjoint. That is, each pixel of the
original image fix, y) exactly belongs to one region R ; (Steudel,A., Glesner,M., 1996).
Fx.y)=\UR, aed R,OR -£ Vjsk (D
There are different methods for image segmentation. Region splitting and merging is one of the methods based on
a process which consists of split and merge operations to be applied on the square regions referred to the
quadruplet tree(QPT). A split operation is started up whenever it is found that the homogeneity of the image data
is insufficient. In this case the region is always split into four subregions in accordance with the four children of
the corresponding node in the QPT. Likewise, four adjacent regions that share the same parent may be merge
together if the homogeneity is sufficient.
A recursion of split and merge operations ends up when all regions found are homogeneous, and each quadruplet
taken together would be inhomogeneous.
3 SPLIT AND MERGE ALGORITHMS
Various split and merge algorithms have been developed. In general, the split and merge technique proceeds as
follows:
l- Define a homogeneity test. This involves defining a homogeneity measure, which may incorporate brightness,
color, texture, or some other specific information. The definition determines a criterion that a region must meet to
pass the homogeneity test.
2- Split the image into four equally sized regions.
3- Calculate the homogeneity measure for each region.
4- If the homogeneity test is passed for the region, then a merge is applied with its neighbor(s). If the criterion is
not met, the region is split.
3- Continue this process until all regions pass the homogeneity test.
There exist many techniques for homogeneity measure.
a)-Pure uniformity
b)- Local mean vs. global mean
C)- Local standard deviation vs. global mean
d)- Variance
e)- Weighted gray level distance
4 MATHEMATICAL MORPHOLOGY
Based on set theory, mathematical morphology provides an approach to the processing of digital image
continuing the geometrical structure of objects. Using appropriate sets known as structuring elements,
mathematical morphology operations can simplify image data while maintaining their shape characteristics and
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000. 37
