THIS IS SIMPLE INTRODUCTION OF RIGION BASED IMAGE SEGMENTATION

1. REGION-BASED IMAGE
SEGMENTATION
By
Ajay Kumar Singh

2. Overview
 Definition
 Need
of segmentation
 Classification of methods
 Region based segmentation

3. Definition

Segmentation refers to the process of
partitioning a image into multiple regions.

Regions:- A group of connected pixels
with similar properties.

Regions are used to interpret images. A
region may correspond to a particular
object, or different parts of an object.

4.  In
most cases, segmentation should
provide a set of regions having the
following properties

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Connectivity and compactness
Regularity of boundaries
Homogeneity in terms of color or texture
Differentiation from neighbor regions

5. Need of segmentation
The goal of segmentation is to simplify the
representation of an image into something that is
more meaningful and easier to analyze.
Image segmentation is typically used to locate
objects and boundaries in images.
For correct interpretation, image must be
partitioned into regions that correspond to
objects or parts of an object.

6. Basic Formulation

Let R represent the entire image region. We
want to partition R into n sub regions, R1,
R2, . . ., Rn, such that:
(a) Summation of Ri =R
(b) Ri is a connected region for i=1, 2, . . , n
(c) Ri intersection Rj =φ for all i and j , I≠ j
(d) P(Ri) = TRUE for i=1, 2, . . . n
(e) P( Ri summation Rj)= False , i ≠ j

7. Basic Formulation




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(a) segmentation must be complete
– all pixels must belong to a region
(b) pixels in a region must be connected
(c) Regions must be disjoint
(d) states that pixels in a region must all share the
same property
– The logic predicate P(Ri) over a region must return
TRUE for each point in that region
(e) indicates that regions are different in the sense of
the predicate P.

8. Segmentation Effect
Region Segmented Image

9. Approaches to
segmentation

Region based approaches


group together pixels with similar
properties
combining proximity and similarity

10. Classification

Region based approaches are based on
pixel properties such as



Homogeneity
Spatial proximity
The most used methods are




Thresholding
Clustering
Region growing
Split and merge

11. Pixel Aggregation (Region
Growing)

The basic idea is to grow from a seed pixel
 At a labeled pixel, check each of its neighbors
 If its attributes are similar to those of the already labeled
pixel,label the neighbor accordingly
 Repeat until there is no more pixel that can be labeled

For example, let
 The attribute of a pixel is its pixel value
 The similarity is defined as the difference between
adjacent pixel values
 If the difference is smaller than a threshold, they are
assigned to the same region, otherwise not

12. Region Growing : Algorithm
a) Chose or determined a group of seed pixel
which can correctly represent the required
region;
 b) Fixed the formula which can contain the
adjacent pixels in the growth;
 c) Made rules or conditions to stop the growth
process


13. Region Split and Merge


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After segmentation the regions may need to be
refined or reformed.
Split operation adds missing boundaries by
splitting regions that contain part of different
objects.
Merge operation eliminates false boundaries and
spurious regions by merging adjacent regions that
belong to the same object.
Split-and-merge in a hierarchical data structure

14. Algorithm: Region Splitting
 Form
initial region in the image
 For each region in an image,
recursively perform:


Compute the variance in the gray values for
the region
If the variance is above a threshold, split
the region along the appropriate boundary

15. 

If some property of a region is not constant
Regular decomposition Methods: divide the region
into a fixed number of equal-sized regions.

16. Algorithm: Region Merging
(1) Form initial regions in the image using thresholding ( or
a similar approach) followed by component labeling.
(2) Prepare a region adjacency graph (RAG) for the image.
(3) For each region in an image, perform the following
steps:
(a) Consider its adjacent region and test to see if they are similar.
(b) For regions that are similar, merge them and modify the RAG.
(4) Repeat step 3 until no regions are merged.

17. Applications
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In image compression
Object recognition
Computer graphics
Medical Imaging
MPEG-4 video object (VO) segmentation

18. References
[1] Rafael C. Gonzalez and Richard E. woods “DIGITAL
IMAGE PROCESSING,ˮ 2011.p. 762-770.
[2] Jun Tang, “A Color Image Segmentation algorithm
Based on Region Growing,ˮ China School of Electronic
Engineering 2010.
[3] Chaobing Huang, Quan Liu, Xiaopeng Li “Color Image
Segmentation by Seeded Regionˮ China, School of
information engineering, 2010.
[4] Tiancan Mei, Chen Zheng, Sidong Zhong, “Hierarchical
Region Based Markov Random Field for Image
Segmentation”,Wuhan,China,2011
[5] en.wikipedia.org/wiki/Region_growing

19. Thanks to All

20. Any Question??