1. Recap
CSC508

2. Low Level Vision Input Processing Output Image sensor data
Neighborhood operators
Mathematical operators
Local contextual operators
Output
Pixel based features
CSC508

3. Mid Level Vision Input Processing Output Pixel based features
Statistical operators
Mathematical operators
Global contextual operators
Output
Objects
CSC508

4. Mid-Level Vision
CSC508

5. Intensity Region Segmentation
CSC508

6. Homogeneous Intensity Regions
Areas of constant intensity within the image
May represent
Objects
Regions of interest
Computed by [one method among many]
Binarization
Connected component analysis
CSC508

7. Binarization (revisited)
Original Gray Level Image
Binary Image
CSC508

8. Binarization (revisited)
CSC508

9. Region Formation 1 2 3 4 Original Gray Level Image Marked Region Image
CSC508

10. Boundary Information Original Gray Level Image Region Boundary Image
CSC508

11. Finding Regions via Connected Component Analysis
In the input image the intensity of each region may be arbitrary
In the binarized image the intensity of each region is 255 (maximum, or at least different from the background)
The goal of connected component analysis is to “label” the pixels of each connected region with a value unique to that region
CSC508

12. Connected Component Analysis
A simple recursive “flood-fill” algorithm will do the trick but…
It is slow
A large object will overflow the stack (memory intensive)
Smarter algorithms can be found in the computer graphics, graph theory, and image processing literature
CSC508

13. Connected Component Analysis
One algorithm works as follows:
Pass 1:
Assign labels to each object pixel
Keep track of neighboring labels that belong to the same object
Pass 2:
Rectify associations
Pass 2 is somewhat complex and comes from a language parsing algorithm – I won’t describe the details here
CSC508

14. Connected Component Analysis1 2 3 4
Original Gray Level Image
Marked Region Image
CSC508

15. Connected Component Analysis
Note that this image is “ideal”
You might expect images as such in a manufacturing environment
In less than ideal cases objects may get broken up or merged by thin lines, etc.
Correction can be made through the use of Morphological operators
Erosion
Dilation
More on Morphological operators next week
CSC508

16. Boundary Extraction
Once you have identified regions it is [conceptually] simple to extract their boundaries
Find the upper-left most pixel of the region
“Walk around” the region always keeping it to your right
Keep track of the directions you step along the way (north, south, east, or west)
Stop when you return back to the beginning
This creates a chain-code of the region boundary
From the chain-code you can compute the perimeter length and other descriptors
CSC508

17. Boundary Chain-Code
“*” marks the starting point (upper-left most pixel of the region)
Resultant chain-code is NEEESESSSWSSWSWWWWNNNNEESENWN
Resultant perimeter is 30 N E E E * S E N S N E
E/W E N S N S E W S N S N W S W W W W S
CSC508

18. Region Description
CSC508

19. Object Description
Now that we have identified objects we need a way to describe them that will facilitate recognition
CSC508

20. Moments A set of descriptors for representing the shape of an object
Typically applied to an identified region but may be used with gray-level
CSC508

21. Discrete Case Moment of an region (general definition) Central moments
0 if not in region
1 if in region
(Object centroid)
CSC508

22. Central Moments
First few central moments:
CSC508

23. Moment Features Centroids (average x and y locations)
Principle axis orientation
CSC508

24. Convex Hull Another useful shape descriptor
The smallest convex border encasing the object
Think of stretching a rubber band around the outside of the object and letting it wrap around it
This will be the convex hull
Input to the algorithm is a set of points
These are the boundary points found previously
CSC508

25. Region Descriptors Region: 1 Convex Hull Area: 27753.0 xBar: 171.26
yBar:
theta:
perimeter: 1098
Convex Hull
Perimeter: 812
CSC508

26. Region Descriptors Region: 2 Convex Hull Area: 10390.0 xBar: 230.82
yBar:
theta: 87.51
perimeter: 580
Convex Hull
perimeter: 515
CSC508

27. Region Descriptors Region: 3 Convex Hull Area: 8049.0 xBar: 362.24
yBar:
theta: -6.62
perimeter: 411
Convex Hull
perimeter: 322
CSC508

28. Region Descriptors Region: 4 Convex Hull Area: 11203.0 xBar: 357.98
yBar:
theta:
perimeter: 648
Convex Hull
perimeter: 520
CSC508

29. Region Descriptors Region: 5 Convex Hull Area: 14880.0 xBar: 92.5
yBar: 366.5
theta: 0.0
perimeter: 492
Convex Hull
perimeter: 484
CSC508

30. Other Features Average Intensity Circularity or Compactness
p: perimeter, a: area
CSC508

31. Things To Do Reading for Next (few) Week(s)
Chapter 14 – Segmentation by Clustering
We’ll consider various clustering methods
Chapter 15 – Segmentation by Models
We’ll analyze the Hough Transform
CSC508

32. Things To Do
Homework
Convex Hull
Find an algorithm for computing the convex hull of a set of points
Describe the algorithm in words
Test the code
Boundary Extraction
Write the code to trace the boundary (chain code) of a specified region
Print out the chain code symbols (start pixel row, column followed by a sequence of N E W S directions)
Moments
Code the μ00, μ10, μ01, μ11, μ20, μ02 moments
You may write in any programming language you choose
Deliverables:
Zipped images in
the source code to with the subject line
CSC508 PROGRAM 3
Due beginning of class in two weeks
(late assignments will be penalized 10%)
I will post test images
CSC508