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Image segmentation

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amal arbab

on 12 May 2015

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Transcript of Image segmentation

1- Thresholding segmentation
Adaptive thresholding

iterative adaptation of the threshold is based on the estimated peak positions. It assumes that :
(i) each peak coincides with the mean grey level for all pixels that relate to that peak .
(ii) the pixel probability decreases monotonically on the absolute difference between the pixel and peak values both for an object and background peak.

Image Segmentation

is the process of partitioning an image into regions
– region: group of connected pixels with similar
properties ( gray levels, colors, textures, motion characteristics ).
 image segmentation
is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super pixels).

Usually image segmentation is an initial step in a series of processes aimed at overall image understanding :

- object recognition
- image compression
- image editing
- image database look-up

The contents
Image Segmentation
The Goal

simplify and/or change 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 (lines, curves, etc.) in images.
More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.

Application of image segmentation
Segmentation Approaches

Simple Thresholding

Adaptive Thresholding

Simple thresholding

- Thresholding is the simplest segmentation method.
- The pixels are partitioned depending on their intensity value.
- Depend on binary region map :
the binary map contains two possibly disjoint regions :
1- pixels with input data values smaller than a threshold labelled with zero (0) .
2- Pixels with input data values that are at or above the threshold labelled with non-zero (1).
The most common image property to threshold is pixel grey level:

g(x,y) = 0 if f(x,y) < T and
g(x,y) = 1 if f(x,y) >=T
..... where T is the threshold.

Such a threshold has two kinds of expected errors:
- assigning a background pixel to the object and
- assigning an object pixel to the background
Threshold problem
1. Compute a threshold T

2. Partition the image into R1, R2 using T
3. Compute the mean values
μ1,μ2 of R1,R2
4. Select a new threshold T=1/2(μ1+μ2)
5. Repeat steps 2-4 until μ1, μ2 do not change
T: mean gray value of image
2- Region segmentation
Pixel connectivity
Region similarity
Pixel connectivity is defined in terms of pixel neighbourhoods.
There are two types of neighbourhood surrounding
a pixel :
{(x−1,y), (x,y+1), (x+1,y), (x,y−1)}
{(x−1,y−1),(x−1,y), (x−1,y+1), (x,y+1),
(x+1,y+1), (x+1,y), (x+1,y−1), (x,y−1)}.
Pixel connectivity
Example :
Region similarity
pixels in the regions are similar with respect to some property (colour, grey level,Density).
A common predicate restricts signal variations over a neighbourhood:
The predicate P(R), where
R denotes a connected region,
is TRUE if |f(x1,y1) - f(x2,y2)| <= D and
FALSE otherwise

Example :
a "good" complete segmentation must satisfy the following criteria:
- All pixels have to be assigned to regions.
- Each pixel has to belong to a single region only.
- Each region is a connected set of pixels.
- Each region has to be uniform with respect to a given predicate.

Presented to you by:
Amal ahmed arbab

Hadeel Taj alser Mohammed

Esraa Khalid Ahmed

Omayman ahmed babekir

- Image Segmentation Definition
-image segmentation Aapplication
- Segmentation Approaches
-- Thresholding Segmentation
-- Region Segmentation
Refrences :

Image Segmentation- E.G.M. Petrakis
Stefano Ferrari - Image processing
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