Параллельные алгоритмы бинаризации изображений

В работе рассматриваются современные методы бинаризации полутоновых изображений, широко используемые в задачах классификации и распознавания образов, а также применяемые в полиграфии при печати для процесса растрирования. Рассмотрены отличительные особенности разных подходов к бинаризации, отмечены их недостатки. Особое внимание уделено блочному алгоритму бинаризации и исследованию эффективности его распараллеливания. Были написаны параллельные реализации блочного алгоритма на CPU с использованием технологии OpenMP и на GPU с использованием технологии CUDA. Было проведено сравнение скорости работы различных реализаций при разных параметрах алгоритма.Modern methods of the binarisation which are widely used in classification problems, pattern recognition and printing industry for the halftoning process are considered in the paper. Distinguishing features of different binarisation approaches were reviewed. The most significant drawbacks of existing algorithms were pointed. The main goal of this work was research on parallelization of block-based binarisation algorithm. Parallel realizations for CPU and GPU were created using OpenMP and CUDA technologies. Efficiencies of different realizations were compared

An Analytical Method for Predicting the Performance of Parallel Image Processing Operations

. This paper presents an analytical performance prediction model and methodology that can be used to predict the execution time, speedup, scalability and similar performance metrics of a large set of image processing operations running on a p-processor parallel system. The model which requires only a few parameters obtainable on a minimal system can help in the systematic design, evaluation and performance tuning of parallel image processing systems. Using the model one can reason about the performance of a parallel image processing system prior to implementation. The method can also support programmers in detecting critical parts of an implementation and system designers in predicting hardware performance and the eect of hardware parameter changes on performance. The execution of parallel image processing operations was studied and operations were arranged in three main problem classes based on data locality and the communication patterns of the algorithms. The core of the method is the derivation of the overhead function, as it is the overhead that determines the achievable speedup. The overheads were examined and modelled for each class. The use of the method is illustrated by four class-representative image processing algorithms: image-scalar addition, convolution, histogram calculation and the Fast Fourier Transform. The developed performance model has been validated on a 16-node parallel machine and it has been shown that the model is able to predict the parallel run-time and other performance metrics of parallel image processing operations accurately. Keywords: performance prediction, image processing, analytical model, overheads, messagepassing, communication pattern 1