1. Parallel Computer Vision and Image Processing Algorithms
Ruoxi Zhang

2. Computer Vision Computer Vision (CV) is a rapidly growing field
intent on enabling computers to process, analyze, and understand the information of images
to produce structured information or make decisions.

3. Applications navigation robotics object detection augmented reality
optical character recognition
face recognition
Computer vision and image processing algorithms are involved with a range of applications:
Augmented reality(AR): is a live, direct or indirect view of a physical, real-world environment
whose elements are augmented by computer generated sensory input
Optical character recognition: computer SW that
recognizes numbers and letters of the alphabet
which are written on paper
The development of computer vision is essential for the advancement of a multitude of areas
including medical, entertainment and security.
extraction of left ventricular contours from Left ventriculograms by means of a neural edge detector AR
quickly scanning a real-world environment
Do simultaneous localization and mapping at the level of objects using valid masks(kernels)
Gaming
Facial feature detection improve face recognition
Created by facial landmark detector
virtual makeover
Tacking library, identify faces, need accuracy

4. For example, …
These two are not the same!

5. Parallelism needs create requirements: more computational resources
nature of image—2D pixel array
SIMD—divide into sub-images (this presentation based on this)
MISD—pipelining
powerful and efficient platform:
the modern GPU
GPGPUs (General Purpose GPUs)
CUDA (Compute Unified Device Architecture) API (Application Programming Interface)
computer vision algorithms require a large number of computations as well as an equally large number of memory values
need of more computational resources is increased
Reasonable time + acceptable accuracy, parallelize the algorithms
Image made by pixels, each has 3 values, RGB
Image can be represented as a 2D array
Parallel image processing tasks can be divided into small steps, build a pipeline machine
At the same time, Graphics Processing Units (GPUs) have proven to be a powerful and efficient computational platform.
The modern GPU can natively perform thousands of parallel computations per clock cycle.
Recent developments have allowed GPUs to be used for more than just graphics processing and rendering
enables software developers to access the GPU's virtual instruction set through standard programming languages such as 'C'

6. Examples edge detection using Sobel Filter
object recognition using Histogram

7. Edge Detection: Sobel Filter
foundational stage in feature detection and extraction
edge: big intensity differences
Sobel Filter computes an approximation of the gradient of the image intensity function
3 x 3 kernel
Edge detection aims to identify image locations in which image brightness(intensity) distinctly differs
Done on a gray scale image
It is a foundational stage in feature detection and feature extraction.
Sobel Filter computes an approximation of the gradient of the image intensity function
Coevolved with a kernel(typically 3 X 3) in horizontal and vertical direction
For Gx, horizontal direction is derivative, and vertical is Gaussian smoothing to reduce noises
Magnitude and orientation

8. Edge Detection: Sobel Filter
CPU:
GPU:
On CPU
On the GPU using CUDA programming
Critical region

9. Edge Detection: Sobel Filter
Kernels
Kernels
Image
Zero padding at the boundary of image
Pts and its 8 neighbors
Convolve with kernel for x direction and kernel for y direction, get Gx and Gy at that pixel location

10. Edge Detection: Sobel Filter
Outperform
Execution time of Sobel implementation

11. Object Recognition: Histogram Construction
a way of representing a distribution—histogram
two parallel tasks: histogram construction and histogram matching
Histograms are used in various fields to quickly profile the distribution of a large amount of data
The practical use of histograms relates to object recognition:
they allow an estimation of recognition without feature correspondence. (where they are)
IMAGE
There are two parallel tasks involved, each with distinct issues in their execution on the GPU:
histogram construction and histogram matching (with candidate)
Construct its histogram, find match (yellow duck rotate)
template
image
input
images

12. Object Recognition: Histogram Construction
the sequential algorithm
calculate normalized histogram

13. Object Recognition: Histogram Construction … … … …
Image
N x N
Quantization
N x N
N by N image
Select the top left corner as example
Select 32 bin size
Quantization 数字化
0-7 -> bin 0
8-15 -> bin 1 …
> bin 31
32 bins total

14. Object Recognition: Histogram Construction
Bin 0
N x N … … … … … … …
Bin 1
N x N
Image
N x N
Quantization
N x N …
32 Binary graph …
0-7 -> bin 0
8-15 -> bin 1 …
> bin 31
32 bins total
Bin 31
N x N … …
Binary graph

15. Object Recognition: Histogram Construction
Bin 0
N x N …
Sum graph 0 … … … … … …
Sum graph 1
Bin 1
N x N
Image
N x N
Quantization
N x N …
Sum graph, count the number of 1’s in the binary graph
Result to a histogram …
0-7 -> bin 0
8-15 -> bin 1 …
> bin 31
32 bins total 31
Bin 31
N x N …
Sum graph 31 …
Binary graph

16. Object Recognition: Histogram Construction
the sequential algorithm
calculate normalized histogram
O( 𝑁 2 )
suppose using 32 bins, compute the task takes 32 * 𝑁 2
use the integral histogram, it takes 3 steps D B
Use the integral histogram
complexity
Sum graph
Orange = A – B – C + D C A

17. Object Recognition: Histogram Construction
the parallel algorithm (thread level)
We consider two methods for thread-level parallelization, depending on how the bin values are shared among threads
1(a) maintains a single shared histogram, whose updates are synchronized via atomic increment instructions
1(b) maintains a private histogram per thread, which is reduced to a global histogram later

18. Object Recognition: Histogram Construction
the parallel algorithm (SIMD)
vectorize bin update with 3 bins and 4-wide SIMD
SIMD-lane-private bins prevents a collision between the 3rd and 4th data elements.
vectorized bin update with 3 bins and 4-wide SIMD
Since the vector width is four, there are four slots for each bin. 方块塔, it is distinguished by the colors
we read the corresponding bin values using a gather instruction
increment the bin values
write the updated bin values using a scatter instruction
reduce into one bin, sum up the private bins using scalar instructions
The privatization prevents a collision between the 3rd and 4th data elements.

19. Object Recognition: Histogram Construction
get integral histograms at each pixel in parallel
apply prefix-sums to the rows of the histogram bins (horizontal cumulative sums)
transpose the array and reapply prefix-sum obtain the integral histograms at each pixel
Computation of the histogram up to location (x, y)
Sum until the current pixel for example (x, y) is added

20. Object Recognition: Histogram Construction
get integral histograms at each pixel in parallel
prescan / parallel prefix sum operation
up-sweep or reduce phase applied to an 8-element array
The parallel prefix sum operation on the GPU consists of two phases:
an upsweep打扫 (or reduce) phase and a down-sweep phase
Up-sweep phase builds a balanced binary tree on the input data and performs one addition per node
Scanning is done from the leaves to the root. (bottom up)
In the down-sweep phase, the tree is traversed from root to the leaves
partial sums from the upsweep phase are aggregated合计的 to obtain the final prefix summed array.
Prescan requires only O(n) operations: 2∗(n−1) additions and (n−1) swaps.

21. Object Recognition: Histogram Construction
get integral histograms at each pixel in parallel
prescan / parallel prefix sum operation
down sweep phase
start by inserting zero at the root of the tree
and on each step, each node at the current level passes its own value to its left child
and the sum of its value and the former value of its left child to its right child
//The algorithm performs O(n log2 n) addition operations. Naïve scan

22. Object Recognition: Histogram Construction
get integral histograms at each pixel
prescan / parallel prefix sum operation
prescan requires only O(n) operations:
2∗(n−1) additions
(n−1) swaps

23. Integral Histogram Comparison
sequential integral histogram

24. Integral Histogram Comparison
parallel integral histogram

25. References
Dan Connors, “Exploring Computer Vision and Image Processing Algorithms in Teaching Parallel Programming,” Department of Electrical Engineering University of Colorado Denver.
M. Chouchene, F. E. Sayadi, Y. Said, M. Atri, and R. Tourki, “Efficient implementation of sobel edge detection algorithm on cpu, gpu and fpga,” International Journal of Advanced Media and Communication, vol. 5, pp. 105 – 117, April 2014.
Poostchi, M., Palaniappan, K., Bunyak, F., Becchi, M., and Seetharaman, G., “Efficient GPU implementation of the integral histogram,” in [Lecture Notes in Computer Science (ACCV Workshop on DeveloperCentered Computer Vision)], 7728(Part I), 266–278 (2012).
Schrider, Christina Da-Wann, "Histogram-Based Template Matching Object Detection in Images With Varying Brightness and Constrast," B.S., Wright State University, 2005, thesis.
W. Jung, J. Park, and J. Lee, “Versatile and scalable parallel histogram construction,” in PACT- 23, 2014.

26. The End