The document discusses image segmentation, defining it as the process of partitioning a digital image into multiple segments for easier analysis. It explains various techniques such as threshold-based, edge-based, region-based, and clustering-based segmentation, detailing their applications and methodologies. The document highlights the importance of image segmentation in object detection, feature extraction, and machine learning, as well as specific techniques like k-means clustering and mean-shift segmentation.

1. SEGMENTATION TECHNIQUES
BY AHMED R. A. SHAMSAN & MOHAMMED ALMOHAMADI

2. WHAT IS IMAGE SEGMENTATION?
What is Image
Segmentation?
• Image segmentation is the process of partitioning a digital image into multiple segments or
regions, each consisting of a group of pixels.
• These segments are also known as image objects.
• The goal is to simplify the image by breaking it down into meaningful components, making it
easier to analyze.
Why Do We
Need Image
Segmentation?
• Image segmentation serves several purposes:
• Object Detection: By identifying distinct segments, we can locate specific objects within an image.
• Feature Extraction: Segmentation helps extract relevant features from different parts of an image.
• Image Understanding: It enables us to understand the content and structure of an image.
• Machine Learning: Segmentation labels can be used for supervised or unsupervised training.

3. SEGMENTATION TECHNIQUES
Threshold-
Based
Segmentation:
Edge-based
Segmentation:
Region Based
Segmentation:
Clustering-
Based
Segmentation
Watershed
Segmentation

4. THRESHOLD-BASED SEGMENTATION
Threshold-based segmentation is a straightforward technique used in
image processing to divide a grayscale image into two distinct
regions based on a threshold value.
Objective of Thresholding:
• The primary goal of thresholding is to create a binary image where each pixel is
assigned one of two values: 0 (black) or 1 (white).
• The decision to assign a pixel to either class depends on whether its intensity value
exceeds a predefined threshold.

5. THRESHOLD-BASED SEGMENTATION
Band-Thresholding
Multi-Thresholding
Semi-Thresholding
Thresholding detection
P-tile-thresholding
histogram shape analysis
Hysteresis Thresholding
Iterative Thresholding Algorithm
Local thresholding
Basic Thresholding

6. segment an image into regions of pixels with gray levels from a set D
and into the background otherwise
"band threshold" refers to a technique used to segment or separate
different regions or objects within an image based on the intensity
values of specific bands or channels. An image typically consists of
multiple color channels, each representing different color information
such as red, green, and blue in RGB images.
BAND-THRESHOLDING

7. Multi-thresholding is a technique used in image
processing to segment an image into multiple
regions based on intensity levels. Thresholding
involves dividing an image into two regions –
one with pixel values below a certain
threshold and another with pixel values
above the threshold. Multi-thresholding extends
this concept by dividing the image into more
than two regions, allowing for finer
segmentation.
MULTI-THRESHOLDING

8. • The process usually begins with analyzing the histogram of the
image, which represents the distribution of pixel intensities.
[1] Image Intensity Histogram:
• Multiple threshold values are selected based on the characteristics
of the histogram. These thresholds divide the intensity range into
several intervals.
[2] Threshold Selection:
• Pixels in the image are then assigned to different segments or
regions based on their intensity values and the selected thresholds.
• For each interval defined by the thresholds, a separate segment is
created. Pixels falling within each interval are grouped together.
[3] Segmentation:
• The segmented regions can be visualized using different colors or
grayscale levels, making it easier to identify distinct features in the
image.(‫لتميزه‬ ‫منفصل‬ ‫بلون‬ ‫قطاع‬ ‫كل‬ ‫)تلوين‬
[4] Region Visualization:
• Multi-thresholding is commonly used in applications where a more
detailed segmentation of an image is required. This can include
medical image analysis, object recognition, and other computer
vision tasks.
Applications:

9. semi-thresholding, the threshold value is determined based on the local
characteristics of the image. This can be particularly useful when dealing
with images that have varying illumination or contrast levels across
different regions. The goal is to adaptively set the threshold for each
pixel or neighborhood, improving segmentation accuracy.
SEMI-THRESHOLDING

10. If some property of an image after
segmentation is known a priori, the
task of threshold selection is simplified,
since the threshold is chosen to ensure
this property is satisfied.
A printed text sheet may be an
example if we know that characters of
the text cover 1/p of the sheet area.
THRESHOLDING DETECTION

11. The process of thresholding detection involves the following steps:
Image Acquisition: The process begins with capturing or loading an image into the system.
Grayscale Conversion: If the image is in color, it is often converted to grayscale to simplify the
analysis. This results in a single-channel image where each pixel represents the intensity of light.
Threshold Selection: The critical step is selecting an appropriate threshold value. This value
determines the dividing line between the pixels that will be considered as part of the object or
region of interest and those that belong to the background.
• Global Thresholding: A single threshold is applied to the entire image.
• Adaptive Thresholding: Different thresholds are applied to different regions of the image
based on local characteristics.
Pixel Classification: Each pixel in the image is then classified into one of the two categories
based on whether its intensity is above or below the selected threshold.
Binary Image Creation: The result is a binary image, where pixels classified as part of the
object are typically represented as white, and those classified as background are represented
as black.

12. choose a threshold T (based on the image histogram) such that 1/p of the image area has
gray values less than T and the rest has gray values larger than T
in text segmentation, prior information about the ratio between the sheet area and character
area can be used
if such a priori information is not available - another property, for example the average
width of lines in drawings, etc. can be used - the threshold can be determined to provide the
required line width in the segmented image
P-TILE-THRESHOLDING

13. if objects have approximately the same gray level
that differs from the gray level of the background
HISTOGRAM SHAPE ANALYSIS
Histogram shape analysis is a fundamental tool in image processing, providing insights into
the characteristics of an image and guiding subsequent processing steps for tasks such as
enhancement, segmentation, and quality assessment.
bi-modal histogram

14. MODE METHOD - FIND THE HIGHEST LOCAL MAXIMA FIRST AND DETECT THE THRESHOLD
AS A MINIMUM BETWEEN THEM
TO AVOID THE DETECTION OF TWO LOCAL MAXIMA BELONGING TO THE SAME GLOBAL
MAXIMUM, A MINIMUM DISTANCE IN GRAY LEVELS BETWEEN THESE MAXIMA IS USUALLY
REQUIRED
OR TECHNIQUES TO SMOOTH HISTOGRAMS ARE APPLIED
BI-MODAL HISTOGRAM THRESHOLD DETECTION ALGORITHMS:

15. HYSTERESIS THRESHOLDING |INTRODUCES TWO THRESHOLDS:
UPPER THRESHOLD (T_UPPER): PIXELS
EXCEEDING THIS VALUE ARE CONFIDENTLY
CLASSIFIED AS “OBJECT.“
LOWER THRESHOLD (T_LOWER): PIXELS
BELOW THIS VALUE ARE DEFINITIVELY
CLASSIFIED AS "BACKGROUND.“
THE MAGIC LIES IN THE INTERMEDIATE
REGION BETWEEN THE THRESHOLDS. PIXELS
WITHIN THIS RANGE ARE ONLY CONSIDERED
"OBJECT" IF THEY ARE CONNECTED TO PIXELS
EXCEEDING THE UPPER THRESHOLD.
THIS "MEMORY" EFFECT ESSENTIALLY IGNORES
ISOLATED NOISE PIXELS THAT FALL WITHIN
THE INTERMEDIATE RANGE, RESULTING IN A
MORE ROBUST SEGMENTATION.

16. ITERATIVE THRESHOLDING ALGORITHM

17. LOCAL THRESHOLDING
NOW, INSTEAD OF USING ONE LINE FOR THE WHOLE PICTURE, WE
USE DIFFERENT LINES FOR DIFFERENT AREAS.
IF A PIXEL LIVES IN A DARK NEIGHBORHOOD, IT MIGHT HAVE A LOWER
THRESHOLD. IF IT’S IN A BRIGHT NEIGHBORHOOD, THE THRESHOLD COULD
BE HIGHER.
THIS WAY, WE ADAPT TO CHANGES IN LIGHTING OR VARIATIONS ACROSS
THE IMAGE.
WHY DO WE USE IT?
LOCAL THRESHOLDING HELPS US FIND OBJECTS IN AN IMAGE EVEN WHEN THE LIGHTING ISN’T
CONSISTENT.
FOR EXAMPLE, IF YOU’RE LOOKING FOR COINS IN A PHOTO, SOME COINS MIGHT BE IN SHADOW WHILE
OTHERS ARE WELL-LIT. LOCAL THRESHOLDING HELPS US FIND ALL OF THEM.

18. EDGE-BASED SEGMENTATION:
• EDGE-BASED SEGMENTATION IDENTIFIES THE BOUNDARIES OR EDGES
BETWEEN DIFFERENT OBJECTS OR REGIONS IN AN IMAGE.
• EDGES OFTEN REPRESENT SIGNIFICANT CHANGES IN INTENSITY OR
COLOR, WHICH ARE CRUCIAL FOR OBJECT DETECTION AND
SEGMENTATION.
• USING BINARY IMAGES FOR EDGE DETECTION: BINARY IMAGES ARE
COMMONLY EMPLOYED IN EDGE DETECTION ALGORITHMS. ONE
POPULAR APPROACH IS TO CONVERT A GRAYSCALE IMAGE TO A BINARY
ONE AND THEN APPLY EDGE DETECTION TECHNIQUES LIKE THE SOBEL
OPERATOR, PREWITT OPERATOR, OR CANNY EDGE DETECTOR.
• BENEFITS OF EDGE-BASED SEGMENTATION: EDGE-BASED
SEGMENTATION IS USEFUL BECAUSE IT HIGHLIGHTS THE BOUNDARIES
BETWEEN OBJECTS
• CHALLENGES: WHILE EDGE-BASED SEGMENTATION IS EFFECTIVE IN MANY
CASES, IT MAY ALSO BE SENSITIVE TO NOISE AND VARIATIONS IN
LIGHTING CONDITIONS. THEREFORE, PRE-PROCESSING STEPS LIKE
SMOOTHING OR FILTERING MAY BE APPLIED TO ENHANCE THE
ROBUSTNESS OF THE SEGMENTATION.

19. REGION BASED SEGMENTATION
• REGION-BASED SEGMENTATION IS A COMPUTER VISION TECHNIQUE THAT INVOLVES DIVIDING AN IMAGE
INTO REGIONS BASED ON CERTAIN CHARACTERISTICS OR CRITERIA. THE GOAL IS TO GROUP TOGETHER
PIXELS OR SUPERPIXELS THAT SHARE SIMILAR PROPERTIES, SUCH AS COLOR, TEXTURE, OR INTENSITY, IN
ORDER TO IDENTIFY MEANINGFUL AND HOMOGENEOUS REGIONS WITHIN AN IMAGE.
• IN SEGMENTATION: USE THE EXTRACTED FEATURES TO PARTITION THE IMAGE INTO REGIONS. THIS IS
OFTEN DONE BY CLUSTERING OR GROUPING PIXELS BASED ON THEIR FEATURE SIMILARITIES.
1.Region Growing
2.Merge Regions
3.Split Region
4.Split and Merge

20. REGION GROWING
• REGION GROWING IS A REGION-BASED SEGMENTATION
TECHNIQUE USED IN IMAGE PROCESSING TO GROUP
PIXELS INTO HOMOGENEOUS REGIONS BASED ON
CERTAIN CRITERIA.
• THE BASIC IDEA BEHIND REGION GROWING IS TO START
WITH AN INITIAL SEED PIXEL OR SET OF PIXELS AND
ITERATIVELY ADD NEIGHBORING PIXELS TO THE REGION IF
THEY SATISFY CERTAIN SIMILARITY CONDITIONS.
Note that the complete segmentation of the image must be It
meets several criteria:
All pixels must be assigned to regions
Each pixel must belong to only one region
Each region must be a connected group of pixels
Each area must be unified

21. REGION BASED SEGMENTATION
• MERGE REGIONS:
repeatedly check neighboring regions, and if they
satisfy the homogeneity criteria, merge them into a
single region. The merging process continues until the
stopping criteria are met.
• STOPPING CRITERIA:
Define conditions for stopping the merging process.
Common stopping criteria include reaching a specified
number of segments or when no further merging
improves homogeneity.
• ADVANTAGES OF REGION MERGING:
It can handle over-segmented images by merging
adjacent regions. It allows for the creation of segments
with variable shapes and sizes. The merging process is
adaptive and depends on local image characteristics.
• ALGORITHM STEPS:
1. BEGIN WITH EACH PIXEL AS A SEPARATE REGION.
2. COMPUTE A REGION DISSIMILARITY MEASURE
FOR ADJACENT REGIONS BASED ON PROPERTIES
SUCH AS PIXEL INTENSITY, COLOR, OR TEXTURE.
3. MERGE THE PAIR OF NEIGHBORING REGIONS
WITH THE LOWEST DISSIMILARITY MEASURE.
4. REPEAT STEPS 2 AND 3 UNTIL A STOPPING
CRITERION IS MET (E.G., A SPECIFIED NUMBER OF
SEGMENTS OR NO FURTHER IMPROVEMENT IN
DISSIMILARITY).

22. SPLIT REGION
IDENTIFY REGIONS FOR SPLITTING:
Identify regions that are not sufficiently homogeneous or show signs of
containing multiple objects.
Define thresholds or criteria for homogeneity that indicate when a
region should be considered for splitting.
• SPLIT OPERATION:
FOR EACH IDENTIFIED REGION TO BE SPLIT:
Choose a splitting strategy, which may involve dividing the region into
smaller subregions based on certain criteria.
This could be done by employing algorithms like region growing,
thresholding, or other segmentation techniques.
• POST-SPLIT HOMOGENEITY CHECK:
After the split operation, reevaluate the homogeneity of the newly
created regions.
Ensure that the split has improved the homogeneity and addressed the
issues identified during the merging step.

23. SPLIT AND MERGE
• Split And Merge Segmentation Is A Technique Used To
Divide An Image Into Meaningful Regions Based On
Certain Criteria.
• The Process Involves Recursively Splitting The Image Into
Smaller Parts (Splitting Step) And Then Merging Similar
Adjacent Regions (Merging Step) To Create A Segmented
Result.
• How Does It Work?
• Splitting (Top-down):
The Image Is Initially Divided Into Quadrants (Usually
Equal-sized Regions).
Each Quadrant Is Evaluated Based On A Homogeneity
Criterion (E.G., Uniformity Of Gray Levels).
If A Quadrant Is Homogeneous (Similar Enough), It Is Not
Split Further.
Otherwise, The Quadrant Is Recursively Split Into Smaller
Regions.

24. CONT’D…
Merging (Bottom-Up):
After splitting, we examine adjacent regions.
If two neighboring regions are similar (based on the same
homogeneity criterion), they are merged into a larger region.
This process continues until all regions pass the homogeneity test.
Homogeneity Criterion:
The key to split and merge segmentation lies in defining
what makes a region homogeneous. Examples of
homogeneity criteria:
Uniformity: A region is homogeneous if its gray scale
levels are constant or within a given threshold.
Local Mean vs. Global Mean: If the mean intensity of a
region is greater than the mean of the entire image, the
region is considered homogeneous.
Variance: The gray level variance within a region should
be below a specified value for it to be homogeneous.

25. CLUSTERING-BASED SEGMENTATION
Clustering-based Segmentation Is A Technique Used In Image Processing To
Partition An Image Into Meaningful And Homogeneous Regions Or Segments
Based On The Similarity Of Pixel Characteristics. The Fundamental Idea Is To
Group Pixels That Share Similar Properties Into Clusters, Making It Easier To
Identify And Analyze Distinct Regions Within An Image.
types of clustering-based segmentation techniques
K-Means Clustering
Mean-Shift
Hierarchical Clustering

26. K-MEANS CLUSTERING
Partition the data points into K clusters
randomly. Find the centroids of each
cluster.
For each data point:
• Calculate the distance from the data
point to each cluster.
• Assign the data point to the closest
cluster.
Recompute the centroid of each
cluster.
Repeat steps 2 and 3 until there is no
further change in the assignment of
data points (or in the centroids).

27. HOW STEPS IS GOING ON

28. K-MEANS CLUSTERING
 EXAMPLE
Duda et al.
 RGB VECTOR

29. CLUSTERING | EXAMPLE

30. K-MEANS CLUSTERING | EXAMPLE
ORIGINAL K=5 K=11

31. K-MEANS, ONLY
COLOR IS USED IN
SEGMENTATION,
FOUR CLUSTERS (OUT
OF 20) ARE SHOWN
HERE.

32. 33
K-MEANS, COLOR AND
POSITION IS USED IN
SEGMENTATION, FOUR
CLUSTERS (OUT OF 20)
ARE SHOWN HERE.
EACH VECTOR IS (R,G,B,X,Y).

33. WHAT IS BEHIND K-MEANS
The core objective of k-means clustering is to minimize the within-cluster
variance. This essentially means k-means tries to create clusters where the data
points within a cluster are as close together as possible, reducing the overall
spread within each group. By achieving this, we can be more confident that
points within a cluster share similar characteristics.

34.  How Mean-Shift Works:
 The goal of mean shift is to identify clusters by
shifting data points toward the mode (highest
density) of nearby points within a certain radius.
 Here’s how it works:
Initialization:
Start by considering each data point as a
potential cluster centroid.
Iterative Steps:
For each data point:
Calculate the mean of all points within a
certain radius (the “kernel”) centered at that
data point.
Shift the data point towards this mean.
Repeat the above step until convergence or a
maximum number of iterations is reached.
MEAN-SHIFT

35.  Versatile technique for clustering-based segmentation
MEAN SHIFT SEGMENTATION

36. Simple Mean Shift procedure:
• Compute mean shift vector
•Translate the Kernel window
by m(x)
2
1
2
1
( )
n
i
i
i
n
i
i
g
h
g
h


 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 


x - x
x
m x x
x - x
g( ) ( )
k

x x
COMPUTING THE MEAN SHIFT
(N) is the number of data points within the
kernel window.
(x_i) represents each data point within the
window.
(K(cdot)) is the kernel function.
(h) is the bandwidth parameter that controls
the size of the kernel window
The kernel window is
then translated by the mean shift
vector (m(x)).
The new position of the kernel
window becomes (x + m(x)).

37. ATTRACTION BASIN
• Attraction basin: the region for which all trajectories lead to
the same mode
• Cluster: all data points in the attraction basin of a mode

38. ATTRACTION BASIN

39. MEAN SHIFT CLUSTERING
The mean shift algorithm seeks modes of the given set of points
• Choose kernel and bandwidth
• For each point:
• Center a window on that point
• Compute the mean of the data in the search window
• Center the search window at the new mean location
• Repeat (b,c) until convergence
• Assign points that lead to nearby modes to the same cluster

40. SEGMENTATION BY MEAN SHIFT
Compute features for each pixel (color, gradients, texture, etc)
Set kernel size for features Kf and position Ks
Initialize windows at individual pixel locations
Perform mean shift for each window until convergence
Merge windows that are within width of Kf and Ks

41. MEAN SHIFT SEGMENTATION RESULTS

43. MEAN SHIFT PROS AND CONS
Pros
• Good general-practice segmentation
• Flexible in number and shape of regions
• Robust to outliers
Cons
• Have to choose kernel size in advance
• Not suitable for high-dimensional features
When to use it
• Oversegmentatoin
• Multiple segmentations
• Tracking, clustering, filtering applications

44. HIERARCHICAL CLUSTERING
Hierarchical clustering creates a hierarchy of clusters by merging or splitting
them based on similarity measures.
How Hierarchical Clustering Works:
The process involves the following steps:
• Initialization:
• Treat each data point as a separate cluster.
• Merging or Splitting:
• Iteratively combine the closest clusters until a stopping criterion is reached.
• Two main approaches:
• Agglomerative (Bottom-Up): Start with individual data points and merge
them into larger clusters.
• Divisive (Top-Down): Begin with all data points in one cluster and
recursively split them.

45. HIERARCHICAL CLUSTERING
A hierarchy might be more nature
Different users might care about different levels of granularity or even
pruning's.

46. HIERARCHICAL CLUSTERING
Top-down (divisive)
• Partition data into 2-groups (e.g., 2-means)
• Recursively cluster each group
Bottom-up (agglomerative)
• Start with every point in its own cluster.
• Repeatedly merge the “closest” two clusters
• Different definitions of “closest” give different algorithms.

47. GOING ON STEPS