Parallel Edge Detection Daniel Dobkin Asaf Nitzan

 Introduction to Image Processing  What are edges? Why do we need to find them? How do we find them?  Motivation to parallelize this process  OpenMP implementation We’ll talk about…

 2-D / 3-D array of pixels  Color channels  RGB – 3 channels  Grayscale – 1 channel  1 byte per channel values of What is an image?

A closer look at pixels R = 225 G = 157 B = 168 R = 201 G = 120 B = 137

Edges  A sharp change in values of adjacent pixels  Motivation to find edges  A very basic feature in image processing

 First, convert image from RGB to Grayscale  Convolve the image with a special 2-D operator  A greater change in intensity indicates a more prominent edge  Sobel operator: Finding Edges

Finding Edges - Example  For Sobel x filter: -617  For Sobel y filter: -669

 Large amount of computations  800 x 600 pixels = 480,000 pixels  5.5 million additions, 2 million multiplications  Especially when it comes to real-time video…  24 fps = 11.5 million pixels  132 million additions, 48 million multiplications… Motivation to Parallelize

 Processors access same shared memory  Each processor performs the region of image assigned to him  Reduced communication - There is no need to broadcast the pixels of the image to all other processors openMP

A B C D openMP Implementation A B C D Master thread Parallel task – Sobel filtering fork join  Multithreading  Master thread forks a number of threads which execute code in parallel Original Image

Load Image from Main memory Allocate memory to new image Set number of threads #pragma omp parallel for \ shared(inputImage, outputImage, width, height)\ private(StartPixel, NumOfThreads, Rank, xPixel, yPixel) for each Pixel in region Convert RGB value to greyscale Compute gradient using Sobel filter Store result in filtered image Join all threads Store new image to disk Pseudocode

openMP Implementation A B C D Original Image Processor 1 Processor 2 Processor 3 Processor 4 Sobel A B C D Filtered Image Main Memory

Speedup Graph  Linear Speedup due to low communication cost