Recap

1. Recap CSC508

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

3. Mid Level Vision • Input • Pixel based features • Processing • 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. 1 2 4 3 Region Formation Original Gray Level Image Marked Region Image CSC508

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

11. Finding Regions viaConnected 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. 1 2 4 3 Connected Component Analysis 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. “*” marks the starting point (upper-left most pixel of the region) Resultant chain-code is NEEESESSSWSSWSWWWWNNNNEESENWN Resultant perimeter is 30 Boundary Chain-Code 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: 1 Area: 27753.0 xBar: 171.26 yBar: 131.89 theta: -32.87 perimeter: 1098 Convex Hull Perimeter: 812 Region Descriptors CSC508

26. Region: 2 Area: 10390.0 xBar: 230.82 yBar: 252.99 theta: 87.51 perimeter: 580 Convex Hull perimeter: 515 Region Descriptors CSC508

27. Region Descriptors • Region: 3 • Area: 8049.0 • xBar: 362.24 • yBar: 223.76 • theta: -6.62 • perimeter: 411 • Convex Hull • perimeter: 322 CSC508

28. Region Descriptors • Region: 4 • Area: 11203.0 • xBar: 357.98 • yBar: 378.14 • theta: -27.80 • perimeter: 648 • Convex Hull • perimeter: 520 CSC508

29. Region: 5 Area: 14880.0 xBar: 92.5 yBar: 366.5 theta: 0.0 perimeter: 492 Convex Hull perimeter: 484 Region Descriptors 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) • Test the code • Moments • Code the μ00, μ10, μ01, μ11, μ20, μ02 moments • Test the code • You may write in any programming language you choose • Deliverables: • Zipped images in email • Email the source code to reinhart@clunet.edu 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