This manuscript addresses the fundamentals of image segmentation.

1. Image Segmentation
Subject: Image Procesing & Computer Vision
Dr. Varun Kumar
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 1 / 13

2. Outlines
1 What is segmentation ?
2 Different approach for image segmentation
Discontinuity based
Region based
3 Different edge detector operator
4 Linking of edge points
Local processing
Global processing
5 References
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 2 / 13

3. Image segmentation
⇒ It is a process for dividing an image into its constituent part.
Q At which level this division should be stopped.
Ans Level of division is application dependent entity.
Detection of movement measurement of vehicle on a road.
Types of image segmentation
1 Discontinuity based approach:
This approach is applicable, where there arise a abrupt changes in the
intensity level in an image.
Isolated points
Lines present in an image
Edges
2 Similarity based approach:
Grouping of those pixels, which are similar in some sense.
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 3 / 13

4. Continued–
Thresholding operation
Region growing based approach
Region splitting and merging
Discontinuity based approach
Using suitable mask, we may be able for detecting
Isolated points
Lines present in an image
Edges
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 4 / 13

5. Continued–
R =
1
i=−1
1
j=−1
Wi,j f (x + i, y + j)
1 Point detection
For point detection
|R| > T
where T is the given threshold.
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 5 / 13

6. Line detection
2 Line detection
Note: If |Ri | > |Rj | ∀ i = j then associated mask is more aligned towards
the direction of ith mask.
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 6 / 13

7. Continued–
3 Edge detection
Note: 2nd order derivative is very sensitive to the noise→ not suitable for
edge detection
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 7 / 13

8. Continued–
Let f (x, y) is a image signal, where
→
f
=
Gx
Gy
=
∂f
∂x
∂f
∂y
or
f = mag(→
f
) = [Gx2
+ Gy2
]1/2
≈ |Gx| + |Gy|
Direction of →
f
α(x, y) = tan−1 Gy
Gx
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 8 / 13

9. Continued–
Previt edge operator
Sobel edge operator
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 9 / 13

10. Results obtained due to Sobel edge operator
Results obtained due to Sobel edge operator
Note: Previt and Sobel operators are the 1st order derivative operators.
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 10 / 13

11. Second derivative operator (Laplacian)
2
f =
∂2
f
∂x2
+
∂2
f
∂y2
Laplacian of Gaussian operator (LoG):
h(x, y) = e− x2+y2
2σ2
Let x2
+ y2
= r2
then
2
h =
r2
− σ2
σ4
exp −
r2
2σ2
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 11 / 13

12. Continued–
LoG and LoG mask
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 12 / 13

13. References
M. Sonka, V. Hlavac, and R. Boyle, Image processing, analysis, and machine vision.
Cengage Learning, 2014.
D. A. Forsyth and J. Ponce, “A modern approach,” Computer vision: a modern
approach, vol. 17, pp. 21–48, 2003.
L. Shapiro and G. Stockman, “Computer vision prentice hall,” Inc., New Jersey,
2001.
R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital image processing using
MATLAB. Pearson Education India, 2004.
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 26 13 / 13