Parallel Image Processing: Active Contour Algorithm Mohammadhossein Behgam

Agenda Overview of Need for parallelism Introduction to Algorithm Parallelizing Summery

What is the problem? And What is the Solution? Image Processing applications can be very computationally demanding due to: Large amount of data Short response time Complexity of the algorithm A wide range of general purpose or custom hardware has been used for image processing. SIMD: using data parallelism, Suitable for low level image analysis where each processor performs a uniform set of operations based on the image data matrix in a fixed amount of time MIMD: using task parallelism and pipelining Suitable for high level image processing simulations, such as pattern recognition, where each processor is assigned an independent task.

Data Parallelism Task Parallelism

Pipeline Parallelism

Active Contour Model Is a framework in computer vision for delineating an object outline from a possibly noisy 2D image. Is greatly used in applications like object tracking, shape recognition, segmentation, edge detection and stereo matching. Understood as a special case of the general technique of matching a deformable model to an image by means of energy minimization.

Parallel Implementation of Active Contour Algorithm

Lowpass Filtering A low pass filter is the basis for most smoothing methods. An image is smoothed by decreasing the disparity between pixel values by averaging nearby pixels.

Edge Detection aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. Edges are defined by first derivative Find dx and dy with convolution Sobel Edge Detection: Gy = Gx =

Thresholding

Distance Transform Calculates the distance to the closest zero pixel for each pixel of the source image. Distance metric Equation Euclidean City block Chess board

LU Decomposition System of linear algebraic equations has form Ax = b Direct method for solving general linear system is by computing LU factorization A = LU System Ax = b then becomes LUx = b Solve lower triangular system Ly = b by forward-substitution to obtain vector y Finally, solve upper triangular system Ux = y by back-substitution to obtain solution x to original system

Parallelizing LU Decomposition

Performance

Results

References Parallel Image Processing, CHARALAMBOS D. STAMOPOULOS Parallel Image Processing System on a Cluster of Personal Computers, J. Barbosa, J. Tavares and A.J. Padilha Parallel and Distributed Algorithms for High Speed Image Processing, Jeffrey M. Squyres, Andrew Lumsdaine Parallel Image Segmentation Using Reduction-Sweeps On Multicore Processors and GPUs, Renato Farias, Ricardo Farias, Ricardo Marroquim A toolkit for parallel image processing, J. M. Squyres, A. Lumsdaine, R. L. Stevenson

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