Segmentation Techniques of Image processing.

1. Submitted by,
G. Midhu Bala and J.Asenath

2. Introduction
 Image processing is any form of signal processing for
which the input is an image, such as a photography or video
frame.
 The output of image processing may be either an image or a
set of characteristics related to the image.
 Image Analysis - to extract high level information on an
image.
 Image Segmentation - to change the representation of an
original image into meaningful portions which makes it
easier to analysis.
 To locate objects and boundaries.

3. Segmentation Techniques
•Thresholding
•Edge Detection
•Color Image Segmentation
•Histogram Based Method

4. Types of images
8 bit image
8 bit image RGB

5. 16 bit image
16 bit RGB

6. 32 bit image
32 bit RGB

7. 8 bit Color
8 bit color RGB

8. RGB Color

9. RGB Stack
Red Green Blue

10. File formats
 JPG  uses lossy compression
 GIF always uses lossless LZW compression, but it is always an
indexed color file (8-bits, 256 colors maximum), which is poor for
24-bit color photos.
 PNG is transparency for 24 bit RGB images. lossless
compression, of different types), but PNG is perhaps slightly slower
to read or write.
 TIF  is lossless (including LZW compression option), which is
considered the highest quality format for commercial work.

11. Test image
.
Original disease free leaf Original affected leaf

12. Thresholding
 Original image into binary image
 Foreground can be separated from the background by selecting the
threshold value
 Global Thresholding -only one threshold value for entire image
 Local Thresholding - different value for different regions
Methods
 Edge Based - detects and links edge pixels to form contour.
 Region Based - detects the entire region

13. Threshold Value : 100 Threshold Value : 150 Threshold Value : 255
Global Thresholding

14. Otsu Method
Threshold Value : 193

15. Edge Detection
 Reduce the amount of data in an image.
 Provides ability to extract the exact edge.
 Corners, lines, curves .
 Meaningful discontinuities in the grey level.
Edge detected image

16. Canny Edge Detection:(Criteria)
 Detection: The probability of detecting real edge
points is maximized and falsely detecting non-edge
points is minimized. This corresponds to maximizing the
signal-to-noise ratio.
 Localization: The detected edges should be as close as
possible to the real edges.
 Number of Responses: One real edge should be result
in more than one detected edge.

17. Canny Edge Detection Algorithm
Smoothing:
Blurring of the image to remove noise.
Finding gradients:
The edges should be marked where the gradients of the
image has large magnitudes.
Non-maximum suppression:
Only local maxima should be marked as edges.
Double thresholding :
Potential edges are determines by thresholding.
Edge tracking by hysteresis:
Final edges are determined by suppressing all edges that are
not connected to a very certain strong edges.

18. Color Image Segmentation:
Color image segmentation is used to extract high level
information of the image based on color. Three phases are
Phase1: Preprocessing:
Morphological methods are applied to remove the noises away
from image which applied to smooth some spots on uniformed
patterns.
Phase2: Transformation:
Color space transformed methods are used to transform other
color space to RGB.

19. Phase3: Segmentation:
Applying clustering algorithm like K-means algorithm for
finding the appropriate cluster numbers and segment images in
different color spaces. The cluster with the maximum average
variance is split into new clusters.
Segmented image

20. Histogram- based methods:
 Compute- Pixels , peaks, valleys
 Locate – clusters
 Recursively applied for finding the smaller clusters.
Distinguishes the two homogeneous regions of the
foreground and background of an image.
Histogram

21. Conclusion
Partitioning an Image using segmentation
techniques leads to extract different regions with
similar attributes . It also detects high level
information of an image for image analysis and
further researches.