1. ECE 692 – Advanced Topics in Computer Vision
Lecture 5 – Segmentation
02/16/16

2. Goals of image processing - revisit
Image improvement – low level IP
Improvement of pictorial information for human interpretation (Improving the visual appearance of images to a human viewer)
Image analysis – high level IP
Processing of scene data for autonomous machine perception (Preparing images for measurement of the features and structures present)

3. HLIP Objects & regions Image Features segmentation Description &
Representation
Understanding,
Decisions,
Knowledge
recognition
Features

4. Image segmentation Based on two properties of gray-level image values
Discontinuity
Edge detection
Similarity
Thresholding
Region growing / splitting / merging

5. Image segmentation Segmentation Detect discontinuity Detect similarity
Edge detection
Gradient operator
Zero crossing (LoG)
edge
Edge linking

6. Edge detection Compute the local derivative
Magnitude of the 1st derivative can be used to detect the presence of an edge
The sign of the 2nd derivative can be used to determine whether an edge pixel lies on the dark or light side of an edge
Zero crossing at the midpoint of a transition in gray level, which provides a powerful approach for locating the edge

7. Gradient operator Use gradient for image differentiation
The gradient of an image f(x,y) at location (x,y) is the vector
Some properties about this gradient vector
It points in the direction of maximum rate of change of image at (x,y)
Magnitude:
Direction (angle is measured w.r.t x axis):

8. Sobel edge detector Advantages:
Providing both differencing and a smoothing effect -1 -2 -1 -1 1 -2 2 1 2 1 -1 1

9. Laplacian edge detector
A second order derivative
Problems:
Very sensitive to noise
Detect double edges
Can’t detect edge direction
Usage:
Find the location of edge using zero-crossings property -1 4

10. Marr and Hildreth’s approach
Smooth the image first to reduce noise
Then compute the 2nd derivative
Finally, find zero-crossings
LoG (Laplacian of Gaussian)

11. Sobel
Threshold=50
Laplacian
Threshold=30
Marr-Hildreth
Std=3.0

12. angiogram
Laplacian, thresh=15
Sobel, thresh=40
Marr-Hildreth (std = 3.0, std=5.0, std=7.0)

13. Edge linking How to deal with gaps in edges?
How to deal with noise in edges?
Linking points by determining whether they lie on a curve of a specified shape

14. Hough transform Can tolerate noise and gaps in edge image
Look for solutions in a parameter space
Classical Hough transform
Detect simple shapes
Line detection
Circle detection
Generalized Hough transform
Detect complicate shapes

15. Line detection

16. Problem with Cartesian coordinate
Polar coordinate

17. Example

18. How to detect a circle?

19. Image segmentation Segmentation Detect discontinuity Detect similarity
Edge detection
Gradient
operator
Zero crossing
(LoG)
Optimal
thresholding
Boundary
thresholding
edge
Hough
Transform
Region growing
Edge linking

20. Optimal thresholding using Otsu’s method
Assumes the image histogram is bi-modal
Maximizes between-class variance

21. Boundary thresholding
The chances of selecting a “good” threshold will be considerably enhanced if the histogram peaks are tall, narrow, symmetric, and separated by deep valleys.
Approach to improve the shape of histogram
Only consider those pixels that lie on or near the boundary (edge)

22. Region growing
Step 1: Assume we find a good threshold, and use it to partition the regions into pure black and white
Step 2: Use different labels to identify different objects
Use region growing to connect parts that should have belonged to the same region
This is called “Connected component analysis”
The regions with the same label generate one segment

23. Example (need 2 scans) 41 541 1 2 21 1 1 2 2 3 3 1 1 4 4 4 4 4