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Parallel Edge Detection

Parallel Edge Detection. Daniel Dobkin Asaf Nitzan. We’ll talk about…. 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. What is an image?. 2-D / 3-D array of pixels

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Parallel Edge Detection

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  1. Parallel Edge Detection Daniel Dobkin AsafNitzan

  2. We’ll talk about… 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

  3. What is an image? • 2-D / 3-D array of pixels • Color channels • RGB – 3 channels • Grayscale – 1 channel • 1 byte per channel values of 0-255

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

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

  6. Finding Edges • 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:

  7. Finding Edges - Example • For Sobel x filter: -617 • For Sobel y filter: -669 • So now we have 2 numbers, now what? • We calculate the magnitude and the orientation of the gradient:

  8. Motivation to Parallelize • 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…

  9. openMP • 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

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

  11. Pseudocode Load Image from Main memory Allocate memory to new image Set number of threads #pragmaomp 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

  12. openMP Implementation Filtered Image Original Image Processor 1 Sobel Processor 2 Sobel Processor 3 Sobel Processor 4 Sobel Main Memory

  13. Speedup Graph • Linear Speedup due to low communication cost http://www.cs.rit.edu/~ptt/courses/4003-531/

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