Image Segmentation

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Image segmentation involves dividing an image into meaningful segments to simplify analysis. It separates an image into regions of similar characteristics. The main types of image segmentation are threshold-based, shape-based, region-based, and contour-based. Challenges to image segmentation include variations in lighting, noise, complex object shapes, and occlusion.

Content
1. What is Segmentation?
2. Examples
3. Why do we need it?
4. Different types
5. Challeges
6. Summary.
2

What is Segmentation?
• From dictionary: The act of dividing something
into segments.
• Definition for us?
segregation or representation of a given
information into something that is easier or
more meaningful for analysis.
Segmentation is a way of narrowing down, from
large amount of data to possibly relevant data.
4

Example(Image)
• Input:
• Segmented result:
8

Uses
In any kind of data analysis like,
• Image analysis
– Object Tracking
– Object Recognition
• Data mining
– Web crawlers
– Search engines
• Speech recognition
9

Types of Image segmentation
1. Threshold based
Issues:
a) Computation of optimal value
of threshold.
b) Light variation across image.
10

Types of Image segmentation(Cont.)
2. Shape based
Issues:
a. Noisy and broken shapes.
b. Occlusion and light variations.
c. Defining complex shape is computationally
complex.
11This example is taken from demo in matlab

Types of Image segmentation(Cont.)
3. Region based
Issues:
a. Finding features which best define our regions.
b. Method to merge segregated regions into an
informative form.
12This example is taken from http://vision.ece.ucsb.edu/segmentation/variational2/

Types of Image segmentation(Cont.)
4. Contour based
Issues:
a. Outliers and contrast with back-ground.
b. Variation in shape
c. Deciding how contour will move to enclose a shape.
13

Common Challenges
• Variations
• Noises
• Complex combinations of different features
• Lighting
• Occlusion
14

Summary
• Segmentation is one of very basic procedure
in data classification and recognition.
• The main challenges are the difficulty of
modelling complex variations.
• Many simple method can do segmentation,
but it depends how we handle the
shortcoming of those approximations.
15

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Image Segmentation

1. Image Segmentation Abhishek Kumar

2. Content 1. What is Segmentation? 2. Examples 3. Why do we need it? 4. Different types 5. Challeges 6. Summary. 2

3. Intuition !! 3

4. What is Segmentation? • From dictionary: The act of dividing something into segments. • Definition for us? segregation or representation of a given information into something that is easier or more meaningful for analysis. Segmentation is a way of narrowing down, from large amount of data to possibly relevant data. 4

5. Example(Signal) Input: 5

6. Example(Signal) Input: Output: 6

7. Example(Image) • Input: 7

8. Example(Image) • Input: • Segmented result: 8

9. Uses In any kind of data analysis like, • Image analysis – Object Tracking – Object Recognition • Data mining – Web crawlers – Search engines • Speech recognition 9

10. Types of Image segmentation 1. Threshold based Issues: a) Computation of optimal value of threshold. b) Light variation across image. 10

11. Types of Image segmentation(Cont.) 2. Shape based Issues: a. Noisy and broken shapes. b. Occlusion and light variations. c. Defining complex shape is computationally complex. 11This example is taken from demo in matlab

12. Types of Image segmentation(Cont.) 3. Region based Issues: a. Finding features which best define our regions. b. Method to merge segregated regions into an informative form. 12This example is taken from http://vision.ece.ucsb.edu/segmentation/variational2/

13. Types of Image segmentation(Cont.) 4. Contour based Issues: a. Outliers and contrast with back-ground. b. Variation in shape c. Deciding how contour will move to enclose a shape. 13

14. Common Challenges • Variations • Noises • Complex combinations of different features • Lighting • Occlusion 14

15. Summary • Segmentation is one of very basic procedure in data classification and recognition. • The main challenges are the difficulty of modelling complex variations. • Many simple method can do segmentation, but it depends how we handle the shortcoming of those approximations. 15

16. Questions? 16

17. Thank You 17

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Image Segmentation

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