Fast CCL(connected component labeling) with GPU

1. ICESS 2016, Takamatsu, Japan
14 ~ 16 Nov. 2016
Young-Min Kang
Tongmyong University
A Parallel Approach to Object Identification
In Large-scale Images
Sung-Soo Kim, ETRI Gyung-Tae Nam, GCSC Inc.

2. Bigger images
• Era of Big data
– Increased sizes of images data
• Image processing
– Heavy Computation
• One of the most fundamental operations
– Object identification/recognition
• Image segmentation
• Connected components labeling

3. Connected component labeling
• Objective
– Pixels in a connected component have an identical labels

4. Parallel image processing
• Most image processing algorithms
– Pixel-wise operations
• can be implemented with pixel-wise threads
• can be efficiently performed in a data-parallel fashion
• GPU
– Data parallel device
– can be easily applied to various image processing methods
GPU:
Many-core architecture

5. Pixel connectivity
• Graph representation
Image Pixel connectivity

6. CCL and parallelism
• CCL with graph traversal
– cannot be easily parallelized
• Traversal = sequential
• GPU based approaches
– has not been very successful

7. Our method
• GPU-based efficient algorithm for CCL
– Data initialization
– Computing column-wise label runs
– Efficient label merge

8. Data initialization
• Each pixel is assigned unique label if it is
turned on

9. Data initialization
• Each pixel is assigned unique label if it is
turned on
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
16 17 18 19 20
21 22 23 24 25

10. Data initialization
• Each pixel is assigned unique label if it is
turned on
1 2 -1 -1 -1
6 7 -1 9 10
11 12 -1 14 -1
-1 -1 -1 19 20
-1 -1 -1 -1 -1

11. Column-wise label runs
• Run
– Block of contiguous object pixels in a column
• Computing column-wise label runs
– Can be done with w threads
h
w

12. Column-wise label runs
• Label change within a column (1 thread)

13. Column-wise label runs
• Graph-based interpretation

14. Column-wise label runs
• Implementation

15. Label merge
• After computing “column-wise label runs”
– We have separate trees to be merges in accordance
with their connectivity
• What is needed
– Checking vertical adjacency

16. Label merge
• Connectivity check

17. Label merge
• Connectivity check

18. Label merge
• Connectivity check

19. Label merge
• Connectivity check

20. Label merge
• Updated hierarchy

21. Why only roots are changed
Let’s merge
OK! I will
follow you

22. Why only roots are changed
Merged tree

23. Previous methods
1. Check the connectivity
2. Update the hierarchy
3. Iterate this process until no update is made
A kind of graph traversal
Heavy computation when the pixels make a
long connected chain

24. Our method
• Label merge is performed with fixed
number of iterations
– The number of iteration
• log2(w)
– Computation cost at every iteration
• reduced to be the half the previous one
• Efficient label merge
• Moreover
– Can be easily parallelized

25. Label merge boundary
• 1st merge
w/2 boundaries
h comparisons in each boundary
 wh/2 threads

26. Label merge boundary
• 2nd merge
w/22 boundaries
h comparisons in each boundary
 wh/22 threads

27. Label merge boundary
• 3rd merge
w/23 boundaries
h comparisons in each boundary
 wh/23 threads

28. Label merge boundary
• Final merge
log2(w) –th merge
Computation cost at the 1st merge: C(1)
Total Cost

30. Performance
• Computational cost for each task
– Cost for Initialization = 1
– 4096x4096 images with different number of connected components
50 labels 1869 labels
initialization 1.0 1.0
column-wise run 1.6 1.6
label merge 3.4 3.6

31. Performance
• Computational cost for each task
– Cost for Initialization = 1
– 4096x4096 images with different number of connected components
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
initialization column-wise run label merge
50 labels
1869 labels

32. Experimental results
• Reference
– Grana’s method implemented with OpenCV
• Two Tests
– Random noise with varying densities
– Object identification with shapes

33. Varying densities
• Image size: 2048x2048

34. Varying densities
• Image size: 2048x2048

35. Varying densities
• Image size: 4096x4096

36. Varying densities
• Image size: 4096x4096

37. Object identification with shapes
• Two spiral curves

38. Object identification with shapes

39. Object identification with shapes
• Stars

40. Object identification with shapes
• Stars

41. Applications
• Object tracking with radar signal

42. Conclusion
• An efficient GPGPU implementation for
CCL
• Data-parallelism of GPU exploited
• Experimental results show its efficiency
• Can be successfully applied to various
applications with large-scale images
– e.g., Object identification from radar signals

43. 감사합니다.
ありがとうございます
谢谢
Thank you
Q & A