Parsytec GCel 3/512计算机视觉算法

P. Nastou, K. Kyrimis, D. Maritsas
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引用次数: 1

摘要

本文描述了一个底层图像处理算法库的实现。该库分为两类算法,一类适用于空间域(局部直方图均衡、局部平均滤波器、中值滤波器、索贝尔边缘检测器和直方图评估),另一类适用于频域(正向和逆离散傅立叶变换、正向离散傅立叶变换的振幅、正向和逆离散余弦变换和巴特沃斯滤波器)。这些算法的效率取决于所使用的处理器的数量,组合不同处理器产生的结果的方法(例如,顺序或使用二叉树),以及组合两个独立产生的结果所需的时间与产生它们所需的时间的比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computer vision algorithms on the Parsytec GCel 3/512
This paper describes the implementation of a library of low-level image processing algorithms. This library is divided into two families of algorithms, one for those that apply to the spatial domain (local histogram equalization, local average filter, median filter, Sobel edge detector, and histogram evaluation), and one for those that apply to the frequency domain (forward and inverse discrete Fourier Transform, amplitude of the forward discrete Fourier transform, forward and inverse discrete cosine transform, and Butterworth filters). The efficiency of these algorithms depends on the number of processors used, the method of combining results produced by different processors (e.g., sequentially or using a binary tree), and the time required for the combination of two independently produced results compared to the time required to produce them.<>
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